diff --git a/pvactools/lib/aggregate_all_epitopes.py b/pvactools/lib/aggregate_all_epitopes.py
index ac111244..8957cb49 100644
--- a/pvactools/lib/aggregate_all_epitopes.py
+++ b/pvactools/lib/aggregate_all_epitopes.py
@@ -234,6 +234,7 @@ def execute(self):
                 'allele_expr_threshold': self.allele_expr_threshold,
                 'maximum_transcript_support_level': self.maximum_transcript_support_level,
                 'percentile_threshold': self.percentile_threshold,
+                'percentile_threshold_strategy': self.percentile_threshold_strategy,
                 'use_allele_specific_binding_thresholds': self.use_allele_specific_binding_thresholds,
                 'mt_top_score_metric': self.mt_top_score_metric,
                 'wt_top_score_metric': self.wt_top_score_metric,
@@ -278,6 +279,7 @@ def __init__(
             expn_val=1,
             maximum_transcript_support_level=1,
             percentile_threshold=None,
+            percentile_threshold_strategy='conservative',
             allele_specific_binding_thresholds=False,
             top_score_metric="median",
             allele_specific_anchors=False,
@@ -291,6 +293,7 @@ def __init__(
         self.binding_threshold = binding_threshold
         self.use_allele_specific_binding_thresholds = allele_specific_binding_thresholds
         self.percentile_threshold = percentile_threshold
+        self.percentile_threshold_strategy = percentile_threshold_strategy
         self.aggregate_inclusion_binding_threshold = aggregate_inclusion_binding_threshold
         self.aggregate_inclusion_count_limit = aggregate_inclusion_count_limit
         self.allele_expr_threshold = trna_vaf * expn_val * 10
@@ -440,6 +443,17 @@ def get_tier(self, mutation, vaf_clonal):
             binding_threshold = self.allele_specific_binding_thresholds[mutation['HLA Allele']]
         else:
             binding_threshold = self.binding_threshold
+        
+        ic50_pass = mutation["{} MT IC50 Score".format(self.mt_top_score_metric)] < binding_threshold
+        percentile_pass = (
+            self.percentile_threshold is None or 
+            mutation["{} MT Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold
+        )
+        binding_pass = (
+            (ic50_pass and percentile_pass) 
+            if self.percentile_threshold_strategy == 'conservative' 
+            else (ic50_pass or percentile_pass)
+        )
 
         anchor_residue_pass = self.is_anchor_residue_pass(mutation)
 
@@ -462,40 +476,28 @@ def get_tier(self, mutation, vaf_clonal):
             vaf_clonal_pass = False
 
         #writing these out as explicitly as possible for ease of understanding
-        if (mutation["{} MT IC50 Score".format(self.mt_top_score_metric)] < binding_threshold and
+        if (binding_pass and
            allele_expr_pass and
            vaf_clonal_pass and
            tsl_pass and
            anchor_residue_pass):
-            if self.percentile_threshold:
-                if mutation["{} MT Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "Pass"
-            else:
-                return "Pass"
+            return "Pass"
 
         #anchor residues
-        if (mutation["{} MT IC50 Score".format(self.mt_top_score_metric)] < binding_threshold and
+        if (binding_pass and
            allele_expr_pass and
            vaf_clonal_pass and
            tsl_pass and
            not anchor_residue_pass):
-            if self.percentile_threshold:
-                if mutation["{} MT Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "Anchor"
-            else:
-                return "Anchor"
+            return "Anchor"
 
         #not in founding clone
-        if (mutation["{} MT IC50 Score".format(self.mt_top_score_metric)] < binding_threshold and
+        if (binding_pass and
            allele_expr_pass and
            not vaf_clonal_pass and
            tsl_pass and
            anchor_residue_pass):
-            if self.percentile_threshold:
-                if mutation["{} MT Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "Subclonal"
-            else:
-                return "Subclonal"
+            return "Subclonal"
 
         #relax expression.  Include sites that have reasonable vaf but zero overall gene expression
         lowexpr=False
@@ -507,16 +509,12 @@ def get_tier(self, mutation, vaf_clonal):
                 lowexpr=True
 
         #if low expression is the only strike against it, it gets lowexpr label (multiple strikes will pass through to poor)
-        if (mutation["{} MT IC50 Score".format(self.mt_top_score_metric)] < binding_threshold and
+        if (binding_pass and
            lowexpr and
            vaf_clonal_pass and
            tsl_pass and
            anchor_residue_pass):
-            if self.percentile_threshold:
-                if mutation["{} MT Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "LowExpr"
-            else:
-                return "LowExpr"
+            return "LowExpr"
 
         #zero expression
         if (mutation["Gene Expression"] == 0 or mutation["Tumor RNA VAF"] == 0) and not lowexpr:
@@ -834,6 +832,7 @@ def __init__(self,
             output_file,
             binding_threshold=500,
             percentile_threshold=None,
+            percentile_threshold_strategy='conservative',
             allele_specific_binding_thresholds=False,
             top_score_metric="median",
             aggregate_inclusion_binding_threshold=5000,
@@ -843,6 +842,7 @@ def __init__(self,
         self.output_file = output_file
         self.binding_threshold = binding_threshold
         self.percentile_threshold = percentile_threshold
+        self.percentile_threshold_strategy = percentile_threshold_strategy
         self.use_allele_specific_binding_thresholds = allele_specific_binding_thresholds
         self.aggregate_inclusion_binding_threshold = aggregate_inclusion_binding_threshold
         self.aggregate_inclusion_count_limit = aggregate_inclusion_count_limit
@@ -963,6 +963,7 @@ def __init__(
         output_file,
         binding_threshold=500,
         percentile_threshold=None,
+        percentile_threshold_strategy='conservative',
         allele_specific_binding_thresholds=False,
         top_score_metric="median",
         read_support=5,
@@ -976,6 +977,7 @@ def __init__(
             output_file,
             binding_threshold=binding_threshold,
             percentile_threshold=percentile_threshold,
+            percentile_threshold_strategy = percentile_threshold_strategy,
             allele_specific_binding_thresholds=allele_specific_binding_thresholds,
             top_score_metric=top_score_metric,
             aggregate_inclusion_binding_threshold=aggregate_inclusion_binding_threshold,
@@ -1014,6 +1016,17 @@ def get_tier(self, mutation, vaf_clonal):
             binding_threshold = self.allele_specific_binding_thresholds[mutation['HLA Allele']]
         else:
             binding_threshold = self.binding_threshold
+        
+        ic50_pass = mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold
+        percentile_pass = (
+            self.percentile_threshold is None or 
+            mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold
+        )
+        binding_pass = (
+            (ic50_pass and percentile_pass) 
+            if self.percentile_threshold_strategy == 'conservative' 
+            else (ic50_pass or percentile_pass)
+        )
 
         low_read_support = False
         if mutation['Read Support'] != 'NA' and mutation['Read Support'] < self.read_support:
@@ -1023,34 +1036,22 @@ def get_tier(self, mutation, vaf_clonal):
         if mutation['Expression'] != 'NA' and mutation['Expression'] < self.expn_val:
             low_expr = True
 
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
           not low_read_support and
           not low_expr):
-            if self.percentile_threshold:
-                if mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold:
-                    return "Pass"
-            else:
-                return "Pass"
+            return "Pass"
 
         #low read support
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
           low_read_support and
           not low_expr):
-            if self.percentile_threshold:
-                if mutation["{} MT IC50 Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "LowReadSupport"
-            else:
-                return "LowReadSupport"
+            return "LowReadSupport"
 
         #low expression
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
           not low_read_support and
           low_expr):
-            if self.percentile_threshold:
-                if mutation["{} MT IC50 Percentile".format(self.mt_top_score_metric)] < self.percentile_threshold:
-                    return "LowExpr"
-            else:
-                return "LowExpr"
+            return "LowExpr"
 
         return "Poor"
 
@@ -1098,13 +1099,20 @@ def get_tier(self, mutation, vaf_clonal):
             binding_threshold = self.allele_specific_binding_thresholds[mutation['HLA Allele']]
         else:
             binding_threshold = self.binding_threshold
+        
+        ic50_pass = mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold
+        percentile_pass = (
+            self.percentile_threshold is None or 
+            mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold
+        )
+        binding_pass = (
+            (ic50_pass and percentile_pass) 
+            if self.percentile_threshold_strategy == 'conservative' 
+            else (ic50_pass or percentile_pass)
+        )
 
-        if mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold:
-            if self.percentile_threshold:
-                if mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold:
-                    return "Pass"
-            else:
-                return "Pass"
+        if binding_pass:
+            return "Pass"
 
         return "Poor"
 
@@ -1117,6 +1125,7 @@ def __init__(
         tumor_purity=None,
         binding_threshold=500,
         percentile_threshold=None,
+        percentile_threshold_strategy='conservative',
         allele_specific_binding_thresholds=False,
         aggregate_inclusion_binding_threshold=5000,
         aggregate_inclusion_count_limit=15,
@@ -1132,6 +1141,7 @@ def __init__(
             output_file,
             binding_threshold=binding_threshold,
             percentile_threshold=percentile_threshold,
+            percentile_threshold_strategy = percentile_threshold_strategy,
             allele_specific_binding_thresholds=allele_specific_binding_thresholds,
             aggregate_inclusion_binding_threshold=aggregate_inclusion_binding_threshold,
             aggregate_inclusion_count_limit=aggregate_inclusion_count_limit,
@@ -1164,6 +1174,17 @@ def get_tier(self, mutation, vaf_clonal):
             binding_threshold = self.allele_specific_binding_thresholds[mutation['HLA Allele']]
         else:
             binding_threshold = self.binding_threshold
+        
+        ic50_pass = mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold
+        percentile_pass = (
+            self.percentile_threshold is None or 
+            mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold
+        )
+        binding_pass = (
+            (ic50_pass and percentile_pass) 
+            if self.percentile_threshold_strategy == 'conservative' 
+            else (ic50_pass or percentile_pass)
+        )
 
         tsl_pass = True
         if mutation["Transcript Support Level"] == "Not Supported":
@@ -1184,26 +1205,18 @@ def get_tier(self, mutation, vaf_clonal):
             vaf_clonal_pass = False
 
         #writing these out as explicitly as possible for ease of understanding
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
            allele_expr_pass and
            vaf_clonal_pass and
            tsl_pass):
-            if self.percentile_threshold:
-                if mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold:
-                    return "Pass"
-            else:
-                return "Pass"
+            return "Pass" 
 
         #not in founding clone
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
            allele_expr_pass and
            not vaf_clonal_pass and
            tsl_pass):
-            if self.percentile_threshold:
-                if mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold:
-                    return "Subclonal"
-            else:
-                return "Subclonal"
+            return "Subclonal"
 
         #relax expression.  Include sites that have reasonable vaf but zero overall gene expression
         lowexpr=False
@@ -1215,15 +1228,11 @@ def get_tier(self, mutation, vaf_clonal):
                 lowexpr=True
 
         #if low expression is the only strike against it, it gets lowexpr label (multiple strikes will pass through to poor)
-        if (mutation["{} IC50 Score".format(self.top_score_metric)] < binding_threshold and
+        if (binding_pass and
            lowexpr and
            vaf_clonal_pass and
            tsl_pass):
-            if self.percentile_threshold:
-                if mutation["{} Percentile".format(self.top_score_metric)] < self.percentile_threshold:
-                    return "LowExpr"
-            else:
-                return "LowExpr"
+            return "LowExpr"
 
         #zero expression
         if (mutation["Gene Expression"] == 0 or mutation["Tumor RNA VAF"] == 0) and not lowexpr:
diff --git a/pvactools/lib/allele_specific_binding_filter.py b/pvactools/lib/allele_specific_binding_filter.py
index 2168d540..f9263ebf 100644
--- a/pvactools/lib/allele_specific_binding_filter.py
+++ b/pvactools/lib/allele_specific_binding_filter.py
@@ -3,7 +3,7 @@
 from pvactools.lib.prediction_class import PredictionClass
 
 class AlleleSpecificBindingFilter:
-    def __init__(self, input_file, output_file, default_threshold, minimum_fold_change, top_score_metric, exclude_nas, percentile_threshold, file_type='pVACseq'):
+    def __init__(self, input_file, output_file, default_threshold, minimum_fold_change, top_score_metric, exclude_nas, percentile_threshold, file_type='pVACseq', percentile_threshold_strategy="conservative"):
         self.input_file = input_file
         self.output_file = output_file
         self.default_threshold = default_threshold
@@ -12,6 +12,7 @@ def __init__(self, input_file, output_file, default_threshold, minimum_fold_chan
         self.exclude_nas = exclude_nas
         self.percentile_threshold = percentile_threshold
         self.file_type = file_type
+        self.percentile_threshold_strategy = percentile_threshold_strategy
 
     def execute(self):
         with open(self.input_file, 'r') as input_fh:
@@ -45,18 +46,23 @@ def execute(self):
                             fold_change = sys.maxsize if entry['Corresponding Fold Change'] == 'NA' else float(entry['Corresponding Fold Change'])
                         percentile_column = 'Best MT Percentile'
 
-                if self.percentile_threshold is not None:
-                    if float(entry[percentile_column]) > self.percentile_threshold:
-                        continue
-
                 threshold = PredictionClass.cutoff_for_allele(entry['HLA Allele'])
                 threshold = self.default_threshold if threshold is None else float(threshold)
 
                 if score == 'NA':
                     if self.exclude_nas:
                         continue
-                elif float(score) > threshold:
-                    continue
+                else:
+                    if self.percentile_threshold is not None:
+                        if self.percentile_threshold_strategy == 'conservative':
+                            if float(score) > threshold or float(entry[percentile_column]) > self.percentile_threshold:
+                                continue
+                        else:
+                            if float(score) > threshold and float(entry[percentile_column]) > self.percentile_threshold:
+                                continue
+                    else:
+                        if float(score) > threshold:
+                            continue
 
                 if self.minimum_fold_change is not None and fold_change < self.minimum_fold_change:
                     continue
diff --git a/pvactools/lib/binding_filter.py b/pvactools/lib/binding_filter.py
index 183af292..eadad92c 100644
--- a/pvactools/lib/binding_filter.py
+++ b/pvactools/lib/binding_filter.py
@@ -7,11 +7,12 @@
 from pvactools.lib.run_utils import *
 
 class BindingFilter:
-    def __init__(self, input_file, output_file, binding_threshold, minimum_fold_change, top_score_metric, exclude_nas, allele_specific_cutoffs, percentile_threshold, file_type='pVACseq'):
+    def __init__(self, input_file, output_file, binding_threshold, minimum_fold_change, top_score_metric, exclude_nas, allele_specific_cutoffs, percentile_threshold, percentile_threshold_strategy='conservative', file_type='pVACseq'):
         self.input_file = input_file
         self.output_file = output_file
         self.binding_threshold = binding_threshold
         self.percentile_threshold = percentile_threshold
+        self.percentile_threshold_strategy = percentile_threshold_strategy
         self.minimum_fold_change = minimum_fold_change
         self.top_score_metric = top_score_metric
         self.exclude_nas = exclude_nas
@@ -22,7 +23,7 @@ def execute(self):
         filter_criteria = []
 
         if self.allele_specific_cutoffs:
-            AlleleSpecificBindingFilter(self.input_file, self.output_file, self.binding_threshold, self.minimum_fold_change, self.top_score_metric, self.exclude_nas, self.percentile_threshold, self.file_type).execute()
+            AlleleSpecificBindingFilter(self.input_file, self.output_file, self.binding_threshold, self.minimum_fold_change, self.top_score_metric, self.exclude_nas, self.percentile_threshold, self.file_type, self.percentile_threshold_strategy).execute()
         else:
             if self.file_type == 'pVACbind' or self.file_type == 'pVACfuse' or self.file_type == 'pVACsplice':
                 if self.top_score_metric == 'median':
@@ -48,8 +49,7 @@ def execute(self):
                 elif self.top_score_metric == 'lowest':
                     column = 'Corresponding Fold Change'
                 filter_criteria.append(FilterCriterion(column, '>=', self.minimum_fold_change, exclude_nas=self.exclude_nas))
-
-            Filter(self.input_file, self.output_file, filter_criteria).execute()
+            Filter(self.input_file, self.output_file, filter_criteria, [], "AND" if self.percentile_threshold_strategy == 'conservative' else "OR").execute()
 
     @classmethod
     def parser(cls, tool):
@@ -78,6 +78,13 @@ def parser(cls, tool):
             help="Report only epitopes where the mutant allele "
                  +"has a percentile rank below this value."
         )
+        parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
         if tool == 'pvacseq':
             parser.add_argument(
                 '-c', '--minimum-fold-change', type=int,
diff --git a/pvactools/lib/filter.py b/pvactools/lib/filter.py
index 24e1b8b3..be0b81fa 100644
--- a/pvactools/lib/filter.py
+++ b/pvactools/lib/filter.py
@@ -5,11 +5,12 @@
 pd.options.mode.chained_assignment = None
 
 class Filter:
-    def __init__(self, input_file, output_file, filter_criteria, int_filter_columns=[]):
+    def __init__(self, input_file, output_file, filter_criteria, int_filter_columns=[], filter_strategy="AND"):
         self.input_file = input_file
         self.output_file = output_file
         self.filter_criteria = filter_criteria
         self.int_filter_columns = int_filter_columns
+        self.filter_strategy = filter_strategy
 
     def execute(self):
         with open(self.input_file, 'r') as read_fh, open(self.output_file, 'w') as write_fh:
@@ -18,18 +19,21 @@ def execute(self):
             writer.writeheader()
             for line in reader:
                 to_filter = False
-                for criterion in self.filter_criteria:
-                    value = line[criterion.column]
-                    if value == 'inf':
-                        value = sys.maxsize
-                    if value == 'NA':
-                        if criterion.exclude_nas:
-                            to_filter = True
-                    elif criterion.skip_value is not None and value == criterion.skip_value:
-                        pass
-                    else:
-                        if not eval("{} {} {}".format(value, criterion.operator, criterion.threshold)):
-                            to_filter = True
+
+                process_criterion = lambda criterion: (
+                    (line[criterion.column] == 'NA' and criterion.exclude_nas) or
+                    (line[criterion.column] != 'NA' and criterion.skip_value != line[criterion.column] and not eval("{} {} {}".format(
+                        line[criterion.column] if line[criterion.column] != 'inf' else sys.maxsize,
+                        criterion.operator,
+                        criterion.threshold
+                    )))
+                )
+
+                if self.filter_strategy == "AND":
+                    to_filter = any(process_criterion(criterion) for criterion in self.filter_criteria)
+                else:
+                    to_filter = all(process_criterion(criterion) for criterion in self.filter_criteria)
+                
                 if not to_filter:
                     writer.writerow(line)
 
diff --git a/pvactools/lib/post_processor.py b/pvactools/lib/post_processor.py
index e8e1cb20..dd5222d3 100644
--- a/pvactools/lib/post_processor.py
+++ b/pvactools/lib/post_processor.py
@@ -88,6 +88,7 @@ def aggregate_all_epitopes(self):
                 tumor_purity=self.tumor_purity,
                 binding_threshold=self.binding_threshold,
                 percentile_threshold=self.percentile_threshold,
+                percentile_threshold_strategy=self.percentile_threshold_strategy,
                 allele_specific_binding_thresholds=self.allele_specific_binding_thresholds,
                 trna_vaf=self.trna_vaf,
                 trna_cov=self.trna_cov,
@@ -106,6 +107,7 @@ def aggregate_all_epitopes(self):
                 binding_threshold=self.binding_threshold,
                 allele_specific_binding_thresholds=self.allele_specific_binding_thresholds,
                 percentile_threshold=self.percentile_threshold,
+                percentile_threshold_strategy=self.percentile_threshold_strategy,
                 top_score_metric=self.top_score_metric,
                 read_support=self.read_support,
                 expn_val=self.expn_val,
@@ -119,6 +121,7 @@ def aggregate_all_epitopes(self):
                 binding_threshold=self.binding_threshold,
                 allele_specific_binding_thresholds=self.allele_specific_binding_thresholds,
                 percentile_threshold=self.percentile_threshold,
+                percentile_threshold_strategy=self.percentile_threshold_strategy,
                 top_score_metric=self.top_score_metric,
                 aggregate_inclusion_binding_threshold=self.aggregate_inclusion_binding_threshold,
                 aggregate_inclusion_count_limit=self.aggregate_inclusion_count_limit,
@@ -130,6 +133,7 @@ def aggregate_all_epitopes(self):
                 tumor_purity=self.tumor_purity,
                 binding_threshold=self.binding_threshold,
                 percentile_threshold=self.percentile_threshold,
+                percentile_threshold_strategy=self.percentile_threshold_strategy,
                 allele_specific_binding_thresholds=self.allele_specific_binding_thresholds,
                 aggregate_inclusion_binding_threshold=self.aggregate_inclusion_binding_threshold,
                 aggregate_inclusion_count_limit=self.aggregate_inclusion_count_limit,
@@ -162,6 +166,7 @@ def execute_binding_filter(self):
             self.exclude_NAs,
             self.allele_specific_binding_thresholds,
             self.percentile_threshold,
+            self.percentile_threshold_strategy,
             self.file_type,
         ).execute()
         print("Completed")
diff --git a/pvactools/lib/run_argument_parser.py b/pvactools/lib/run_argument_parser.py
index 9e5e513f..9456d448 100644
--- a/pvactools/lib/run_argument_parser.py
+++ b/pvactools/lib/run_argument_parser.py
@@ -71,6 +71,13 @@ def __init__(self, tool_name, input_file_help):
             help="Report only epitopes where the mutant allele "
                  +"has a percentile rank below this value."
         )
+        parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
         parser.add_argument(
             '--allele-specific-binding-thresholds',
             help="Use allele-specific binding thresholds. To print the allele-specific binding thresholds run `%s allele_specific_cutoffs`. " % tool_name
diff --git a/pvactools/tools/pvacbind/binding_filter.py b/pvactools/tools/pvacbind/binding_filter.py
index e1dafe10..6cc3394c 100644
--- a/pvactools/tools/pvacbind/binding_filter.py
+++ b/pvactools/tools/pvacbind/binding_filter.py
@@ -9,7 +9,7 @@ def main(args_input = sys.argv[1:]):
     parser = define_parser()
     args = parser.parse_args(args_input)
 
-    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, 'pVACbind').execute()
+    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, args.percentile_threshold_strategy, 'pVACbind').execute()
 
 if __name__ == "__main__":
     main()
diff --git a/pvactools/tools/pvacbind/generate_aggregated_report.py b/pvactools/tools/pvacbind/generate_aggregated_report.py
index a7e45687..7a8d35a4 100644
--- a/pvactools/tools/pvacbind/generate_aggregated_report.py
+++ b/pvactools/tools/pvacbind/generate_aggregated_report.py
@@ -38,6 +38,13 @@ def define_parser():
              + "has percentile scores below this value and in the \"Relaxed\" tier "
              + "when the mutant allele has percentile scores below double this value.",
     )
+    parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
     parser.add_argument(
         '--aggregate-inclusion-binding-threshold', type=int,
         help="Threshold for including epitopes when creating the aggregate report",
@@ -72,6 +79,7 @@ def main(args_input = sys.argv[1:]):
         binding_threshold=args.binding_threshold,
         allele_specific_binding_thresholds=args.allele_specific_binding_thresholds,
         percentile_threshold=args.percentile_threshold,
+        percentile_threshold_strategy=args.percentile_threshold_strategy,
         top_score_metric=args.top_score_metric,
         aggregate_inclusion_binding_threshold=args.aggregate_inclusion_binding_threshold,
         aggregate_inclusion_count_limit=args.aggregate_inclusion_count_limit,
diff --git a/pvactools/tools/pvacbind/run.py b/pvactools/tools/pvacbind/run.py
index ad21b0f5..7fb6b14b 100644
--- a/pvactools/tools/pvacbind/run.py
+++ b/pvactools/tools/pvacbind/run.py
@@ -69,6 +69,7 @@ def main(args_input = sys.argv[1:]):
         'top_score_metric'          : args.top_score_metric,
         'binding_threshold'         : args.binding_threshold,
         'percentile_threshold'      : args.percentile_threshold,
+        'percentile_threshold_strategy': args.percentile_threshold_strategy,
         'allele_specific_binding_thresholds': args.allele_specific_binding_thresholds,
         'net_chop_fasta'            : args.input_file,
         'net_chop_method'           : args.net_chop_method,
diff --git a/pvactools/tools/pvacfuse/binding_filter.py b/pvactools/tools/pvacfuse/binding_filter.py
index 6ce7b473..cbb52f18 100644
--- a/pvactools/tools/pvacfuse/binding_filter.py
+++ b/pvactools/tools/pvacfuse/binding_filter.py
@@ -9,7 +9,7 @@ def main(args_input = sys.argv[1:]):
     parser = define_parser()
     args = parser.parse_args(args_input)
 
-    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, 'pVACfuse').execute()
+    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, args.percentile_threshold_strategy, 'pVACfuse').execute()
 
 if __name__ == "__main__":
     main()
diff --git a/pvactools/tools/pvacfuse/generate_aggregated_report.py b/pvactools/tools/pvacfuse/generate_aggregated_report.py
index dc4a103d..d37bc065 100644
--- a/pvactools/tools/pvacfuse/generate_aggregated_report.py
+++ b/pvactools/tools/pvacfuse/generate_aggregated_report.py
@@ -38,6 +38,13 @@ def define_parser():
              + "has percentile scores below this value and in the \"Relaxed\" tier "
              + "when the mutant allele has percentile scores below double this value.",
     )
+    parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
     parser.add_argument(
         '--aggregate-inclusion-binding-threshold', type=int,
         help="Threshold for including epitopes when creating the aggregate report",
@@ -82,6 +89,7 @@ def main(args_input = sys.argv[1:]):
         binding_threshold=args.binding_threshold,
         allele_specific_binding_thresholds=args.allele_specific_binding_thresholds,
         percentile_threshold=args.percentile_threshold,
+        percentile_threshold_strategy=args.percentile_threshold_strategy,
         top_score_metric=args.top_score_metric,
         read_support=args.read_support,
         expn_val=args.expn_val,
diff --git a/pvactools/tools/pvacfuse/run.py b/pvactools/tools/pvacfuse/run.py
index dcde1933..ad540bdf 100644
--- a/pvactools/tools/pvacfuse/run.py
+++ b/pvactools/tools/pvacfuse/run.py
@@ -141,6 +141,7 @@ def main(args_input = sys.argv[1:]):
         'top_score_metric'          : args.top_score_metric,
         'binding_threshold'         : args.binding_threshold,
         'percentile_threshold'      : args.percentile_threshold,
+        'percentile_threshold_strategy': args.percentile_threshold_strategy,
         'allele_specific_binding_thresholds': args.allele_specific_binding_thresholds,
         'net_chop_method'           : args.net_chop_method,
         'net_chop_threshold'        : args.net_chop_threshold,
diff --git a/pvactools/tools/pvacseq/binding_filter.py b/pvactools/tools/pvacseq/binding_filter.py
index 5da66e8c..9fc0edda 100644
--- a/pvactools/tools/pvacseq/binding_filter.py
+++ b/pvactools/tools/pvacseq/binding_filter.py
@@ -9,7 +9,7 @@ def main(args_input = sys.argv[1:]):
     parser = define_parser()
     args = parser.parse_args(args_input)
 
-    BindingFilter(args.input_file, args.output_file, args.binding_threshold, args.minimum_fold_change, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold).execute()
+    BindingFilter(args.input_file, args.output_file, args.binding_threshold, args.minimum_fold_change, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, args.percentile_threshold_strategy).execute()
 
 if __name__ == "__main__":
     main()
diff --git a/pvactools/tools/pvacseq/generate_aggregated_report.py b/pvactools/tools/pvacseq/generate_aggregated_report.py
index 5b870395..49893e5f 100644
--- a/pvactools/tools/pvacseq/generate_aggregated_report.py
+++ b/pvactools/tools/pvacseq/generate_aggregated_report.py
@@ -43,6 +43,13 @@ def define_parser():
              + "has percentile scores below this value and in the \"Relaxed\" tier "
              + "when the mutant allele has percentile scores below double this value.",
     )
+    parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
     parser.add_argument(
         '--aggregate-inclusion-binding-threshold', type=int,
         help="Binding threshold for including epitopes when creating the aggregate report",
@@ -119,6 +126,7 @@ def main(args_input = sys.argv[1:]):
         binding_threshold=args.binding_threshold,
         allele_specific_binding_thresholds=args.allele_specific_binding_thresholds,
         percentile_threshold=args.percentile_threshold,
+        percentile_threshold_strategy=args.percentile_threshold_strategy,
         trna_vaf=args.trna_vaf,
         trna_cov=args.trna_cov,
         expn_val=args.expn_val,
diff --git a/pvactools/tools/pvacseq/run.py b/pvactools/tools/pvacseq/run.py
index 518c4adf..27f13307 100644
--- a/pvactools/tools/pvacseq/run.py
+++ b/pvactools/tools/pvacseq/run.py
@@ -85,6 +85,7 @@ def main(args_input = sys.argv[1:]):
         'top_score_metric'          : args.top_score_metric,
         'binding_threshold'         : args.binding_threshold,
         'percentile_threshold'      : args.percentile_threshold,
+        'percentile_threshold_strategy': args.percentile_threshold_strategy,
         'allele_specific_binding_thresholds': args.allele_specific_binding_thresholds,
         'minimum_fold_change'       : args.minimum_fold_change,
         'net_chop_method'           : args.net_chop_method,
diff --git a/pvactools/tools/pvacsplice/binding_filter.py b/pvactools/tools/pvacsplice/binding_filter.py
index baa2fa23..f75bf6f3 100644
--- a/pvactools/tools/pvacsplice/binding_filter.py
+++ b/pvactools/tools/pvacsplice/binding_filter.py
@@ -9,7 +9,7 @@ def main(args_input = sys.argv[1:]):
     parser = define_parser()
     args = parser.parse_args(args_input)
 
-    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, file_type='pVACsplice').execute()
+    BindingFilter(args.input_file, args.output_file, args.binding_threshold, None, args.top_score_metric, args.exclude_NAs, args.allele_specific_binding_thresholds, args.percentile_threshold, args.percentile_threshold_strategy, 'pVACsplice').execute()
 
 if __name__ == "__main__":
     main()
diff --git a/pvactools/tools/pvacsplice/generate_aggregated_report.py b/pvactools/tools/pvacsplice/generate_aggregated_report.py
index 0a788e36..5d3b69b8 100644
--- a/pvactools/tools/pvacsplice/generate_aggregated_report.py
+++ b/pvactools/tools/pvacsplice/generate_aggregated_report.py
@@ -43,6 +43,13 @@ def define_parser():
              + "has percentile scores below this value and in the \"Relaxed\" tier "
              + "when the mutant allele has percentile scores below double this value.",
     )
+    parser.add_argument(
+            '--percentile-threshold-strategy',
+            choices=['conservative', 'exploratory'],
+            help="Specify the candidate inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default)."
+                 + " The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+            default="conservative",
+        )
     parser.add_argument(
         '--aggregate-inclusion-binding-threshold', type=int,
         help="Threshold for including epitopes when creating the aggregate report",
@@ -95,6 +102,7 @@ def main(args_input = sys.argv[1:]):
         binding_threshold=args.binding_threshold,
         allele_specific_binding_thresholds=args.allele_specific_binding_thresholds,
         percentile_threshold=args.percentile_threshold,
+        percentile_threshold_strategy=args.percentile_threshold_strategy,
         trna_vaf=args.trna_vaf,
         trna_cov=args.trna_cov,
         expn_val=args.expn_val,
diff --git a/pvactools/tools/pvacsplice/run.py b/pvactools/tools/pvacsplice/run.py
index dead2562..eca886f5 100644
--- a/pvactools/tools/pvacsplice/run.py
+++ b/pvactools/tools/pvacsplice/run.py
@@ -153,6 +153,7 @@ def main(args_input = sys.argv[1:]):
         'top_score_metric'          : args.top_score_metric,
         'binding_threshold'         : args.binding_threshold,
         'percentile_threshold'      : args.percentile_threshold,
+        'percentile_threshold_strategy': args.percentile_threshold_strategy,
         'allele_specific_binding_thresholds': args.allele_specific_binding_thresholds,
         'aggregate_inclusion_binding_threshold' : args.aggregate_inclusion_binding_threshold,
         'aggregate_inclusion_count_limit': args.aggregate_inclusion_count_limit,
diff --git a/pvactools/tools/pvacvector/run.py b/pvactools/tools/pvacvector/run.py
index a90a9391..64ab63f2 100644
--- a/pvactools/tools/pvacvector/run.py
+++ b/pvactools/tools/pvacvector/run.py
@@ -140,10 +140,18 @@ def find_min_scores(parsed_output_files, current_output_dir, args, min_scores, m
                     threshold = float(args.binding_threshold) if threshold is None else float(threshold)
                 else:
                     threshold = float(args.binding_threshold)
-                if score < threshold:
-                    junctions_with_good_binders.add(index)
-                if args.percentile_threshold is not None and percentile < args.percentile_threshold:
-                    junctions_with_good_binders.add(index)
+                if args.percentile_threshold is None:
+                    if score < threshold:
+                        junctions_with_good_binders.add(index)
+                else:
+                    if args.percentile_threshold_strategy == 'conservative':
+                        #a conservative strategy would invalidate junctions that fail the binding threshold or the percentile threshold
+                        if score < threshold or percentile < args.percentile_threshold:
+                            junctions_with_good_binders.add(index)
+                    elif args.percentile_threshold_strategy == 'exploratory':
+                        #a exploratory strategy is more relaxed and only invalidates a junction if it fails both the binding and percentile thresholds
+                        if score < threshold and percentile < args.percentile_threshold:
+                            junctions_with_good_binders.add(index)
 
                 if index not in junction_min_scores:
                     #"initialize" with the first score encountered
@@ -524,7 +532,7 @@ def main(args_input=sys.argv[1:]):
             'A vaccine design using the parameters specified could not be found. Some options that you may want to consider:\n' +
             '1) decreasing the acceptable junction binding score to allow more possible connections (-b parameter)\n' +
             '2) using the "median" binding score instead of the "best" binding score for each junction, (best may be too conservative, -m parameter)\n' +
-            '3) if running with a percentile threshold set, either remove this parameter or reduce the acceptable threshold to allow more possible connections (--percentile-threshold parameter)\n' +
+            '3) if running with a percentile threshold set, either remove this parameter, reduce the acceptable threshold to allow more possible connections (--percentile-threshold parameter) or use the \'exploratory\' strategry (--percentile-threshold-strategy parameter)\n' +
             '4) increase the number of peptides that can be excluded from the vector (--allow-n-peptide-exclusion parameter)'
         )
 
diff --git a/pvactools/tools/pvacview/anchor_and_helper_functions.R b/pvactools/tools/pvacview/anchor_and_helper_functions.R
index 5aa920b6..e95c8efc 100644
--- a/pvactools/tools/pvacview/anchor_and_helper_functions.R
+++ b/pvactools/tools/pvacview/anchor_and_helper_functions.R
@@ -254,7 +254,7 @@ calculate_mutation_info <- function(metrics_data_row) {
   return(diff_positions)
 }
 ##Generate Tiering for given variant with specific cutoffs
-tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cutoff, mutation_pos_list, hla_allele, tsl, meta_data, anchor_mode, use_allele_specific_binding_thresholds, binding_threshold, percentile_threshold) {
+tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cutoff, mutation_pos_list, hla_allele, tsl, meta_data, anchor_mode, use_allele_specific_binding_thresholds, binding_threshold, percentile_threshold, percentile_threshold_strategy) {
   mt_binding <- as.numeric(variant_info["IC50 MT"])
   wt_binding <- as.numeric(variant_info["IC50 WT"])
   mt_percent <- as.numeric(variant_info["%ile MT"])
@@ -264,16 +264,13 @@ tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cuto
   rna_vaf <- as.numeric(variant_info["RNA VAF"])
   rna_depth <- as.numeric(variant_info["RNA Depth"])
   allele_expr <- as.numeric(variant_info["Allele Expr"])
-  if (use_allele_specific_binding_thresholds && hla_allele %in% names(meta_data[["allele_specific_binding_thresholds"]][hla_allele])) {
-    binding_threshold <- as.numeric(meta_data[["allele_specific_binding_thresholds"]][hla_allele])
-  }
-  trna_vaf <- as.numeric(meta_data["trna_vaf"])
-  trna_cov <- as.numeric(meta_data["trna_cov"])
   percentile_filter <- FALSE
   if (!is.null(percentile_threshold)) {
     percentile_threshold <- as.numeric(percentile_threshold)
     percentile_filter <- TRUE
   }
+  trna_vaf <- as.numeric(meta_data["trna_vaf"])
+  trna_cov <- as.numeric(meta_data["trna_cov"])
   tsl_max <- as.numeric(meta_data["maximum_transcript_support_level"])
   mutation_pos_list <- mutation_pos_list[["Pos"]]
   if (anchor_mode == "default") {
@@ -284,6 +281,20 @@ tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cuto
       anchor_list <- c(1, 2, nchar(variant_info[["Best Peptide"]]), nchar(variant_info[["Best Peptide"]]) - 1)
     }
   }
+  if (use_allele_specific_binding_thresholds && hla_allele %in% names(meta_data[["allele_specific_binding_thresholds"]][hla_allele])) {
+    binding_threshold <- as.numeric(meta_data[["allele_specific_binding_thresholds"]][hla_allele])
+  }
+  ic50_pass <- (mt_binding < binding_threshold)
+  percentile_pass <- TRUE
+  if (percentile_filter && mt_percent > percentile_threshold) {
+    percentile_pass <- FALSE
+  }
+  binding_pass <- TRUE
+  if (percentile_threshold_strategy == 'conservative') {
+    binding_pass <- (ic50_pass && percentile_pass)
+  } else {
+    binding_pass <- (ic50_pass || percentile_pass)
+  }
   anchor_residue_pass <- TRUE
   # if all of mutated positions in anchors
   if (grepl("-", mutation_pos_list, fixed = TRUE)) {
@@ -325,33 +336,15 @@ tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cuto
     vaf_clonal_pass <- FALSE
   }
   ## Assign Tiering
-  if ((mt_binding < binding_threshold) && allele_expr_pass && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return("Pass")
-      }
-    }else {
-      return("Pass")
-    }
+  if (binding_pass && allele_expr_pass && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    return("Pass")
   }
-  
-  if ((mt_binding < binding_threshold) && allele_expr_pass && vaf_clonal_pass && tsl_pass && !anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return("Anchor")
-      }
-    }else {
-      return("Anchor")
-    }
+
+  if (binding_pass && allele_expr_pass && vaf_clonal_pass && tsl_pass && !anchor_residue_pass) {
+    return("Anchor")
   }
-  if ((mt_binding < binding_threshold) && allele_expr_pass && !vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return("Subclonal")
-      }
-    }else {
-      return("Subclonal")
-    }
+  if (binding_pass && allele_expr_pass && !vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    return("Subclonal")
   }
   lowexpr <- FALSE
   if (!is.na(rna_vaf) && !is.na(gene_expr) && !is.na(rna_depth)) {
@@ -359,14 +352,8 @@ tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cuto
       lowexpr <- TRUE
     }
   }
-  if ((mt_binding < binding_threshold) && lowexpr && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return("LowExpr")
-      }
-    }else {
-      return("LowExpr")
-    }
+  if (binding_pass && lowexpr && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    return("LowExpr")
   }
   if (!is.na(allele_expr) && ((gene_expr == 0) || (rna_vaf == 0)) && !lowexpr) {
     return("NoExpr")
@@ -374,7 +361,7 @@ tier <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cuto
   return("Poor")
 }
 #Determine the Tier Count for given variant with specific cutoffs
-tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cutoff, mutation_pos_list, hla_allele, tsl, meta_data, anchor_mode, allele_specific_binding_thresholds, use_allele_specific_binding_thresholds, binding_threshold, percentile_threshold) {
+tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_expr_cutoff, mutation_pos_list, hla_allele, tsl, meta_data, anchor_mode, allele_specific_binding_thresholds, use_allele_specific_binding_thresholds, binding_threshold, percentile_threshold, percentile_threshold_strategy) {
   mt_binding <- as.numeric(variant_info["IC50 MT"])
   wt_binding <- as.numeric(variant_info["IC50 WT"])
   mt_percent <- as.numeric(variant_info["%ile MT"])
@@ -384,9 +371,6 @@ tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_e
   rna_vaf <- as.numeric(variant_info["RNA VAF"])
   rna_depth <- as.numeric(variant_info["RNA Depth"])
   allele_expr <- as.numeric(variant_info["Allele Expr"])
-  if (use_allele_specific_binding_thresholds && hla_allele %in% names(meta_data[["allele_specific_binding_thresholds"]][hla_allele])) {
-    binding_threshold <- as.numeric(meta_data[["allele_specific_binding_thresholds"]][hla_allele])
-  }
   trna_vaf <- as.numeric(meta_data["trna_vaf"])
   trna_cov <- as.numeric(meta_data["trna_cov"])
   percentile_filter <- FALSE
@@ -395,6 +379,20 @@ tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_e
     percentile_filter <- TRUE
   }
   tsl_max <- as.numeric(meta_data["maximum_transcript_support_level"])
+  if (use_allele_specific_binding_thresholds && hla_allele %in% names(meta_data[["allele_specific_binding_thresholds"]][hla_allele])) {
+    binding_threshold <- as.numeric(meta_data[["allele_specific_binding_thresholds"]][hla_allele])
+  }
+  ic50_pass <- (mt_binding < binding_threshold)
+  percentile_pass <- TRUE
+  if (percentile_filter && mt_percent > percentile_threshold) {
+    percentile_pass <- FALSE
+  }
+  binding_pass <- TRUE
+  if (percentile_threshold_strategy == 'conservative') {
+    binding_pass <- (ic50_pass && percentile_pass)
+  } else {
+    binding_pass <- (ic50_pass || percentile_pass)
+  }
   mutation_pos_list <- mutation_pos_list[["Pos"]]
   if (anchor_mode == "default") {
     anchor_list <- c(1, 2, nchar(variant_info[["Best Peptide"]]), nchar(variant_info[["Best Peptide"]]) - 1)
@@ -447,33 +445,15 @@ tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_e
     vaf_clonal_pass <- FALSE
   }
   ## Pass
-  if ((mt_binding < binding_threshold) && allele_expr_pass && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return(1)
-      }
-    }else {
-      return(1)
-    }
+  if (binding_pass && allele_expr_pass && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    return(1)
   }
   ## Anchor
-  if ((mt_binding < binding_threshold) && allele_expr_pass && vaf_clonal_pass && tsl_pass && !anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return(5)
-      }
-    }else {
-      return(5)
-    }
+  if (binding_pass && allele_expr_pass && vaf_clonal_pass && tsl_pass && !anchor_residue_pass) {
+    return(5)
   }
-  if ((mt_binding < binding_threshold) && allele_expr_pass && !vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        return(6)
-      }
-    }else {
-      return(6)
-    }
+  if (binding_pass && allele_expr_pass && !vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    return(6)
   }
   lowexpr <- FALSE
   if (!is.na(rna_vaf) && !is.na(gene_expr) && !is.na(rna_depth)) {
@@ -481,21 +461,11 @@ tier_numbers <- function(variant_info, anchor_contribution, dna_cutoff, allele_e
       lowexpr <- TRUE
     }
   }
-  if ((mt_binding < binding_threshold) && (lowexpr) && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
-    if (percentile_filter) {
-      if (mt_percent <= percentile_threshold) {
-        if (allele_expr > 0) {
-          return(7)
-        }else if ((gene_expr == 0) && (rna_depth > trna_cov) && (rna_vaf > trna_vaf)) {
-          return(8)
-        }
-      }
-    }else {
-      if (allele_expr > 0) {
-        return(7)
-      }else if ((gene_expr == 0) && (rna_depth > trna_cov) && (rna_vaf > trna_vaf)) {
-        return(8)
-      }
+  if (binding_pass && lowexpr && vaf_clonal_pass && tsl_pass && anchor_residue_pass) {
+    if (allele_expr > 0) {
+      return(7)
+    }else if ((gene_expr == 0) && (rna_depth > trna_cov) && (rna_vaf > trna_vaf)) {
+      return(8)
     }
   }
   if (!is.na(allele_expr) && ((gene_expr == 0) || (rna_vaf == 0)) && !lowexpr) {
diff --git a/pvactools/tools/pvacview/server.R b/pvactools/tools/pvacview/server.R
index b642b71e..4535ad54 100644
--- a/pvactools/tools/pvacview/server.R
+++ b/pvactools/tools/pvacview/server.R
@@ -93,6 +93,7 @@ server <- shinyServer(function(input, output, session) {
     use_allele_specific_binding_thresholds = NULL,
     aggregate_inclusion_binding_threshold = NULL,
     percentile_threshold = NULL,
+    percentile_threshold_strategy = NULL,
     allele_specific_binding_thresholds = NULL,
     allele_expr = NULL,
     anchor_mode = NULL,
@@ -128,6 +129,7 @@ server <- shinyServer(function(input, output, session) {
     df$allele_specific_binding_thresholds <- df$metricsData$`allele_specific_binding_thresholds`
     df$aggregate_inclusion_binding_threshold <- df$metricsData$`aggregate_inclusion_binding_threshold`
     df$percentile_threshold <- df$metricsData$`percentile_threshold`
+    df$percentile_threshold_strategy <- df$metricsData$`percentile_threshold_strategy`
     df$dna_cutoff <- df$metricsData$vaf_clonal
     df$allele_expr <- df$metricsData$allele_expr_threshold
     df$anchor_mode <- ifelse(df$metricsData$`allele_specific_anchors`, "allele-specific", "default")
@@ -154,7 +156,7 @@ server <- shinyServer(function(input, output, session) {
       df$comments <- data.frame(matrix("No comments", nrow = nrow(df$mainTable)), ncol = 1)
     }
     df$mainTable <- df$mainTable[, columns_needed]
-    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
+    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
     df$mainTable$`Gene of Interest` <- apply(df$mainTable, 1, function(x) {any(x["Gene"] == df$gene_list)})
     rownames(df$comments) <- df$mainTable$ID
     df$mainTable$`Scaled BA` <- apply(df$mainTable, 1, function(x) scale_binding_affinity(df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, x["Allele"], x["IC50 MT"]))
@@ -218,6 +220,7 @@ server <- shinyServer(function(input, output, session) {
        df$use_allele_specific_binding_thresholds <- df$metricsData$`use_allele_specific_binding_thresholds`
        df$aggregate_inclusion_binding_threshold <- df$metricsData$`aggregate_inclusion_binding_threshold`
        df$percentile_threshold <- df$metricsData$`percentile_threshold`
+       df$percentile_threshold_strategy <- df$metricsData$`percentile_threshold_strategy`
        df$dna_cutoff <- df$metricsData$vaf_clonal
        df$allele_expr <- df$metricsData$allele_expr_threshold
        df$anchor_mode <- ifelse(df$metricsData$`allele_specific_anchors`, "allele-specific", "default")
@@ -245,7 +248,7 @@ server <- shinyServer(function(input, output, session) {
          df$comments <- data.frame(matrix("No comments", nrow = nrow(df$mainTable)), ncol = 1)
        }
        df$mainTable <- df$mainTable[, columns_needed]
-       df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
+       df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
        df$mainTable$`Gene of Interest` <- apply(df$mainTable, 1, function(x) {any(x["Gene"] == df$gene_list)})
        if ("Comments" %in% colnames(df$mainTable)) {
          df$comments <- data.frame(data = df$mainTable$`Comments`, nrow = nrow(df$mainTable), ncol = 1)
@@ -340,6 +343,15 @@ server <- shinyServer(function(input, output, session) {
     current_percentile <- df$percentile_threshold
     numericInput("percentile_threshold", "Percentile Threshold", current_percentile, min = 0, max = 100, step = 0.01, width = 500)
   })
+  output$percentile_threshold_strategy_ui <- renderUI({
+    current_percentile_threshold_strategy <- df$percentile_threshold_strategy
+    radioButtons(
+      "percentile_threshold_strategy",
+      "Specify the candidate binding score and percentile inclusion strategy. The 'conservative' option requires a candidate to pass BOTH the binding threshold and percentile threshold (default). The 'exploratory' option requires a candidate to pass EITHER the binding threshold or the percentile threshold.",
+      c("conservative", "exploratory"),
+      selected = current_percentile_threshold_strategy
+    )
+  })
   output$dna_cutoff_ui <- renderUI({
     current_dna_cutoff <- df$dna_cutoff
     numericInput("dna_cutoff", "Clonal DNA VAF (Anything lower than 1/2 of chosen VAF level will be considered subclonal)", current_dna_cutoff, min = 0, max = 1, step = 0.01, width = 500)
@@ -358,6 +370,7 @@ server <- shinyServer(function(input, output, session) {
     } else {
       df$percentile_threshold <- input$percentile_threshold
     }
+    df$percentile_threshold_strategy <- input$percentile_threshold_strategy
     df$dna_cutoff <- input$dna_cutoff
     df$allele_expr <- input$allele_expr
     df$allele_specific_anchors <- input$use_anchor
@@ -368,8 +381,8 @@ server <- shinyServer(function(input, output, session) {
     }else {
       df$anchor_mode <- "default"
     }
-    df$mainTable$`Tier` <- apply(df$mainTable, 1, function(x) tier(x, df$anchor_contribution, input$dna_cutoff, input$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
-    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, input$dna_cutoff, input$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
+    df$mainTable$`Tier` <- apply(df$mainTable, 1, function(x) tier(x, df$anchor_contribution, input$dna_cutoff, input$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
+    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, input$dna_cutoff, input$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
     df$mainTable$`Scaled BA` <- apply(df$mainTable, 1, function(x) scale_binding_affinity(df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, x["Allele"], x["IC50 MT"]))
     df$mainTable$`Scaled percentile` <- apply(df$mainTable, 1, function(x) {ifelse((is.null(df$percentile_threshold) || is.na(df$percentile_threshold)), as.numeric(x["%ile MT"]), as.numeric(x["%ile MT"]) / (df$percentile_threshold))})
     if (is.null(df$percentile_threshold) || is.na(df$percentile_threshold)) {
@@ -396,13 +409,14 @@ server <- shinyServer(function(input, output, session) {
     df$allele_specific_binding_thresholds <- df$metricsData$`allele_specific_binding_thresholds`
     df$use_allele_specific_binding_thresholds <- df$metricsData$`use_allele_specific_binding_thresholds`
     df$percentile_threshold <- df$metricsData$`percentile_threshold`
+    df$percentile_threshold_strategy <- df$metricsData$`percentile_threshold_strategy`
     df$dna_cutoff <- df$metricsData$`vaf_clonal`
     df$allele_expr <- df$metricsData$`allele_expr`
     df$anchor_mode <- ifelse(df$metricsData$`allele_specific_anchors`, "allele-specific", "default")
     df$allele_specific_anchors <- df$metricsData$`allele_specific_anchors`
     df$anchor_contribution <- df$metricsData$`anchor_contribution_threshold`
-    df$mainTable$`Tier` <- apply(df$mainTable, 1, function(x) tier(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
-    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold))
+    df$mainTable$`Tier` <- apply(df$mainTable, 1, function(x) tier(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
+    df$mainTable$`Tier Count` <- apply(df$mainTable, 1, function(x) tier_numbers(x, df$anchor_contribution, df$dna_cutoff, df$allele_expr, x["Pos"], x["Allele"], x["TSL"], df$metricsData[1:15], df$anchor_mode, df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, df$percentile_threshold, df$percentile_threshold_strategy))
     df$mainTable$`Scaled BA` <- apply(df$mainTable, 1, function(x) scale_binding_affinity(df$allele_specific_binding_thresholds, df$use_allele_specific_binding_thresholds, df$binding_threshold, x["Allele"], x["IC50 MT"]))
     df$mainTable$`Scaled percentile` <- apply(df$mainTable, 1, function(x) {ifelse(is.null(df$percentile_threshold), as.numeric(x["%ile MT"]), as.numeric(x["%ile MT"]) / (df$percentile_threshold))})
     if (is.null(df$percentile_threshold)) {
@@ -439,7 +453,7 @@ server <- shinyServer(function(input, output, session) {
     data <- data.frame(
       "Parameter" = c("Tumor Purity", "VAF Clonal", "VAF Subclonal", "Allele Expression for Passing Variants",
                       "Binding Threshold", "Binding Threshold for Inclusion into Metrics File", "Maximum TSL",
-                      "Percentile Threshold", "Allele Specific Binding Thresholds",
+                      "Percentile Threshold", "Percentile Threshold Strategy", "Allele Specific Binding Thresholds",
                       "MT Top Score Metric", "WT Top Score Metric",
                       "Allele Specific Anchors Used", "Anchor Contribution Threshold"),
       "Value" = c(if (is.null(df$metricsData$tumor_purity)) {"NULL"}else {df$metricsData$tumor_purity},
@@ -447,6 +461,7 @@ server <- shinyServer(function(input, output, session) {
                   df$metricsData$binding_threshold, df$metricsData$`aggregate_inclusion_binding_threshold`,
                   df$metricsData$maximum_transcript_support_level,
                   if (is.null(df$metricsData$percentile_threshold)) {"NULL"}else { df$metricsData$percentile_threshold},
+                  df$metricsData$percentile_threshold_strategy,
                   df$metricsData$use_allele_specific_binding_thresholds,
                   df$metricsData$mt_top_score_metric, df$metricsData$wt_top_score_metric,
                   df$metricsData$allele_specific_anchors, df$metricsData$anchor_contribution_threshold)
@@ -475,7 +490,7 @@ server <- shinyServer(function(input, output, session) {
     data <- data.frame(
       "Parameter" = c("VAF Clonal", "VAF Subclonal", "Allele Expression for Passing Variants",
                       "Binding Threshold", "Binding Threshold for Inclusion into Metrics File", "Maximum TSL",
-                      "Percentile Threshold", "Allele Specific Binding Thresholds",
+                      "Percentile Threshold", "Percentile Threshold Strategy", "Allele Specific Binding Thresholds",
                       "MT Top Score Metric", "WT Top Score Metric",
                       "Allele Specific Anchors Used", "Anchor Contribution Threshold"),
       "Value" = c(
@@ -486,6 +501,7 @@ server <- shinyServer(function(input, output, session) {
         df$metricsData$`aggregate_inclusion_binding_threshold`,
         df$metricsData$maximum_transcript_support_level,
         if (is.null(df$percentile_threshold) || is.na(df$percentile_threshold)) {"NULL"}else { df$percentile_threshold},
+        df$percentile_threshold_strategy,
         df$use_allele_specific_binding_thresholds,
         df$metricsData$mt_top_score_metric,
         df$metricsData$wt_top_score_metric,
diff --git a/pvactools/tools/pvacview/ui.R b/pvactools/tools/pvacview/ui.R
index e2730359..e6c67b71 100644
--- a/pvactools/tools/pvacview/ui.R
+++ b/pvactools/tools/pvacview/ui.R
@@ -98,6 +98,7 @@ explore_tab <- tabItem(
                     uiOutput("binding_threshold_ui"),
                     uiOutput("allele_specific_binding_ui"),
                     uiOutput("percentile_threshold_ui"),
+                    uiOutput("percentile_threshold_strategy_ui"),
                     uiOutput("dna_cutoff_ui"),
                     uiOutput("allele_expr_ui"),
                     h5("For further explanations on these inputs, please refer to the ", tags$a(href = "https://pvactools.readthedocs.io/en/latest/pvacview/pvacseq_module/pvacseq_upload.html#visualize-and-explore", "pVACview documentation.", target = "_blank")),
diff --git a/tests/test_data/aggregate_all_epitopes/HCC1395.output.metrics.json b/tests/test_data/aggregate_all_epitopes/HCC1395.output.metrics.json
index 25d0b803..54dca258 100644
--- a/tests/test_data/aggregate_all_epitopes/HCC1395.output.metrics.json
+++ b/tests/test_data/aggregate_all_epitopes/HCC1395.output.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Median",
   "wt_top_score_metric": "Median",
diff --git a/tests/test_data/aggregate_all_epitopes/output.allele_specific_binding_thresholds.metrics.json b/tests/test_data/aggregate_all_epitopes/output.allele_specific_binding_thresholds.metrics.json
index 7aaed642..2ed818d6 100644
--- a/tests/test_data/aggregate_all_epitopes/output.allele_specific_binding_thresholds.metrics.json
+++ b/tests/test_data/aggregate_all_epitopes/output.allele_specific_binding_thresholds.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": true,
   "mt_top_score_metric": "Median",
   "wt_top_score_metric": "Median",
diff --git a/tests/test_data/aggregate_all_epitopes/output.lowest_top_score_metric.metrics.json b/tests/test_data/aggregate_all_epitopes/output.lowest_top_score_metric.metrics.json
index c5e5fc40..856bc24e 100644
--- a/tests/test_data/aggregate_all_epitopes/output.lowest_top_score_metric.metrics.json
+++ b/tests/test_data/aggregate_all_epitopes/output.lowest_top_score_metric.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Best",
   "wt_top_score_metric": "Corresponding",
diff --git a/tests/test_data/aggregate_all_epitopes/output.metrics.json b/tests/test_data/aggregate_all_epitopes/output.metrics.json
index 1600d58c..fca1f0cf 100644
--- a/tests/test_data/aggregate_all_epitopes/output.metrics.json
+++ b/tests/test_data/aggregate_all_epitopes/output.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Median",
   "wt_top_score_metric": "Median",
diff --git a/tests/test_data/aggregate_all_epitopes/output.na_mutation_position.metrics.json b/tests/test_data/aggregate_all_epitopes/output.na_mutation_position.metrics.json
index 4b6f4a1f..8f21a9dd 100644
--- a/tests/test_data/aggregate_all_epitopes/output.na_mutation_position.metrics.json
+++ b/tests/test_data/aggregate_all_epitopes/output.na_mutation_position.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Median",
   "wt_top_score_metric": "Median",
diff --git a/tests/test_data/pvacseq/MHC_Class_I/Test.all_epitopes.aggregated.metrics.json b/tests/test_data/pvacseq/MHC_Class_I/Test.all_epitopes.aggregated.metrics.json
index da507f59..7f68f217 100644
--- a/tests/test_data/pvacseq/MHC_Class_I/Test.all_epitopes.aggregated.metrics.json
+++ b/tests/test_data/pvacseq/MHC_Class_I/Test.all_epitopes.aggregated.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Best",
   "wt_top_score_metric": "Corresponding",
diff --git a/tests/test_data/pvacseq/MHC_Class_II/Test.all_epitopes.aggregated.metrics.json b/tests/test_data/pvacseq/MHC_Class_II/Test.all_epitopes.aggregated.metrics.json
index 7c42aa77..7a0be83e 100644
--- a/tests/test_data/pvacseq/MHC_Class_II/Test.all_epitopes.aggregated.metrics.json
+++ b/tests/test_data/pvacseq/MHC_Class_II/Test.all_epitopes.aggregated.metrics.json
@@ -10,6 +10,7 @@
   "allele_expr_threshold": 2.5,
   "maximum_transcript_support_level": 1,
   "percentile_threshold": null,
+  "percentile_threshold_strategy": "conservative",
   "use_allele_specific_binding_thresholds": false,
   "mt_top_score_metric": "Best",
   "wt_top_score_metric": "Corresponding",
diff --git a/tests/test_filter.py b/tests/test_filter.py
index dae128a4..058a332d 100644
--- a/tests/test_filter.py
+++ b/tests/test_filter.py
@@ -183,3 +183,59 @@ def test_inf(self):
             os.path.join(self.test_data_path, "output.inf.tsv"),
             False
         ))
+    
+    def test_conservative(self):
+        output_file = tempfile.NamedTemporaryFile()
+        self.assertFalse(Filter(
+            os.path.join(
+                self.test_data_path,
+                'Test.combined.parsed.tsv'
+            ),
+            output_file.name,
+            [FilterCriterion(
+                "Median MT IC50 Score",
+                "<",
+                "500",
+                exclude_nas=False
+            ), FilterCriterion(
+                "Corresponding Fold Change",
+                "<",
+                "16000",
+                exclude_nas=False
+            )],
+            [],
+            "AND"
+        ).execute())
+        self.assertTrue(cmp(
+            output_file.name,
+            os.path.join(self.test_data_path, "Test.filtered.lt.tsv"),
+            False
+        ))
+    
+    def test_exploratory(self):
+        output_file = tempfile.NamedTemporaryFile()
+        self.assertFalse(Filter(
+            os.path.join(
+                self.test_data_path,
+                'Test.combined.parsed.tsv'
+            ),
+            output_file.name,
+            [FilterCriterion(
+                "Median MT IC50 Score",
+                "<",
+                "500",
+                exclude_nas=False
+            ), FilterCriterion(
+                "Corresponding Fold Change",
+                ">",
+                "16000",
+                exclude_nas=False
+            )],
+            [],
+            "OR"
+        ).execute())
+        self.assertTrue(cmp(
+            output_file.name,
+            os.path.join(self.test_data_path, "Test.filtered.lt.tsv"),
+            False
+        ))