diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
index a9aabd70a1..7d1f8f595e 100644
--- a/docs/source/release_notes.rst
+++ b/docs/source/release_notes.rst
@@ -5,6 +5,7 @@
     * Enhancements
         * Added caching capability for ensemble training during ``AutoMLSearch`` :pr:`3257`
     * Fixes
+        * Fixed ``get_pipelines`` to reset pipeline threshold for binary cases :pr:`3360`
     * Changes
     * Documentation Changes
     * Testing Changes
diff --git a/evalml/automl/automl_search.py b/evalml/automl/automl_search.py
index 3cbba80ecb..6d2ed50815 100644
--- a/evalml/automl/automl_search.py
+++ b/evalml/automl/automl_search.py
@@ -1276,7 +1276,7 @@ def get_pipeline(self, pipeline_id):
             )
         new_pipeline = pipeline.new(parameters, random_seed=self.random_seed)
         if is_binary(self.problem_type):
-            new_pipeline.threshold = pipeline.threshold
+            new_pipeline.threshold = None
         return new_pipeline
 
     def describe_pipeline(self, pipeline_id, return_dict=False):
diff --git a/evalml/tests/automl_tests/test_automl_search_classification.py b/evalml/tests/automl_tests/test_automl_search_classification.py
index 613bc142d7..88a6c771a7 100644
--- a/evalml/tests/automl_tests/test_automl_search_classification.py
+++ b/evalml/tests/automl_tests/test_automl_search_classification.py
@@ -479,7 +479,7 @@ def test_non_optimizable_threshold(AutoMLTestEnv, X_y_binary):
     )
 
 
-def test_optimize_threshold_maintained(AutoMLTestEnv, X_y_binary):
+def test_optimize_threshold_get_pipeline_reset(AutoMLTestEnv, X_y_binary):
     X, y = X_y_binary
     automl = AutoMLSearch(
         X_train=X,
@@ -502,7 +502,7 @@ def test_optimize_threshold_maintained(AutoMLTestEnv, X_y_binary):
 
     best_pipeline_id = automl.rankings["id"][0]
     best_get = automl.get_pipeline(best_pipeline_id)
-    assert best_get.threshold == 0.8
+    assert best_get.threshold is None
 
 
 def test_describe_pipeline_objective_ordered(X_y_binary, caplog):
@@ -1270,3 +1270,29 @@ def test_automl_passes_allow_long_running_models(
         estimators.extend(["CatBoost Classifier", "XGBoost Classifier"])
 
     assert "Dropping estimators {}".format(", ".join(sorted(estimators))) in caplog.text
+
+
+def test_automl_threshold_score(fraud_100):
+    X, y = fraud_100
+    X_train, X_valid, y_train, y_valid = split_data(X, y, "binary")
+
+    automl = AutoMLSearch(
+        X_train,
+        y_train,
+        "binary",
+        max_batches=4,
+        ensembling=True,
+        verbose=False,
+        automl_algorithm="default",
+        train_best_pipeline=True,
+    )
+    automl.search()
+
+    bp = automl.best_pipeline
+    best_pipeline_score = bp.score(X_valid, y_valid, objectives=["F1"])
+    bp_ranking_id = automl.rankings.iloc[0].id
+    pl = automl.get_pipeline(bp_ranking_id)
+    pl = automl.train_pipelines([pl])[pl.name]
+    manual_score = pl.score(X_valid, y_valid, objectives=["F1"])
+    assert bp.name == pl.name
+    assert best_pipeline_score == manual_score