diff --git a/deepchecks/tabular/checks/model_evaluation/simple_model_comparison.py b/deepchecks/tabular/checks/model_evaluation/simple_model_comparison.py
index b5e6438274..b92d23cd84 100644
--- a/deepchecks/tabular/checks/model_evaluation/simple_model_comparison.py
+++ b/deepchecks/tabular/checks/model_evaluation/simple_model_comparison.py
@@ -371,7 +371,8 @@ def add_condition_gain_greater_than(self,
             Used in classification models to limit condition only to given classes.
         average : bool , default: False
             Used in classification models to flag if to run condition on average of classes, or on
-            each class individually
+            each class individually. If any scorer that return a single value is used, this parameter
+            is ignored (will act as if average=True).
         """
         name = f'Model performance gain over simple model is greater than {format_percent(min_allowed_gain)}'
         if classes:
@@ -390,8 +391,13 @@ def condition(result: Dict, include_classes=None, average=False, max_gain=None,
     task_type = result['type']
     scorers_perfect = result['scorers_perfect']
 
+    # If the depth of the nested scores dict is 2, average is not relevant and is set to True
+    inner_dict = scores[list(scores.keys())[0]]
+    inner_inner_dict = inner_dict[list(inner_dict.keys())[0]]
+    force_average = isinstance(inner_inner_dict, Number)
+
     passed_condition = True
-    if task_type in [TaskType.MULTICLASS, TaskType.BINARY] and not average:
+    if task_type in [TaskType.MULTICLASS, TaskType.BINARY] and not average and not force_average:
         passed_metrics = {}
         failed_classes = defaultdict(dict)
         perfect_metrics = []
@@ -433,7 +439,7 @@ def condition(result: Dict, include_classes=None, average=False, max_gain=None,
         passed_metrics = {}
         failed_metrics = {}
         perfect_metrics = []
-        if task_type in [TaskType.MULTICLASS, TaskType.BINARY]:
+        if task_type in [TaskType.MULTICLASS, TaskType.BINARY] and not force_average:
             scores = average_scores(scores, include_classes)
         for metric, models_scores in scores.items():
             # If origin model is perfect, skip the gain calculation
diff --git a/tests/tabular/checks/model_evaluation/simple_model_comparison_test.py b/tests/tabular/checks/model_evaluation/simple_model_comparison_test.py
index 017d89adc2..d8e8fdee08 100644
--- a/tests/tabular/checks/model_evaluation/simple_model_comparison_test.py
+++ b/tests/tabular/checks/model_evaluation/simple_model_comparison_test.py
@@ -194,6 +194,23 @@ def test_condition_pass_for_multiclass_avg(iris_split_dataset_and_model):
     ))
 
 
+def test_condition_pass_for_custom_scorer(iris_dataset_single_class, iris_random_forest_single_class):
+    train_ds = iris_dataset_single_class
+    test_ds = iris_dataset_single_class
+    clf = iris_random_forest_single_class
+    # Arrange
+    check = SimpleModelComparison(scorers=['f1'], strategy='most_frequent').add_condition_gain_greater_than(0.43)
+    # Act X
+    result = check.run(train_ds, test_ds, clf)
+    # Assert
+    assert_that(result.conditions_results, has_items(
+        equal_condition_result(
+            is_pass=True,
+            details='Found metrics with perfect score, no gain is calculated: [\'f1\']',
+            name='Model performance gain over simple model is greater than 43%')
+    ))
+
+
 def test_condition_pass_for_multiclass_avg_with_classes(iris_split_dataset_and_model):
     train_ds, test_ds, clf = iris_split_dataset_and_model
     # Arrange