diff --git a/feature_engine/selection/smart_correlation_selection.py b/feature_engine/selection/smart_correlation_selection.py
index 8fd9afa60..baae9ef4e 100644
--- a/feature_engine/selection/smart_correlation_selection.py
+++ b/feature_engine/selection/smart_correlation_selection.py
@@ -29,6 +29,7 @@
     _check_contains_inf,
     _check_contains_na,
     check_X,
+    check_y,
 )
 from feature_engine.selection.base_selector import BaseSelector
 
@@ -64,6 +65,7 @@ class SmartCorrelatedSelection(BaseSelector):
     - Feature with the highest cardinality (greatest number of unique values).
     - Feature with the highest variance.
     - Feature with the highest importance according to an estimator.
+    - Feature with the highest correlation with the target variable.
 
     SmartCorrelatedSelection() returns a dataframe containing from each group of
     correlated features, the selected variable, plus all the features that were
@@ -99,8 +101,8 @@ class SmartCorrelatedSelection(BaseSelector):
     {missing_values}
 
     selection_method: str, default= "missing_values"
-        Takes the values "missing_values", "cardinality", "variance" and
-        "model_performance".
+        Takes the values "missing_values", "cardinality", "variance",
+        "model_performance", and "corr_with_target".
 
         **"missing_values"**: keeps the feature from the correlated group with the least
         missing observations.
@@ -115,6 +117,11 @@ class SmartCorrelatedSelection(BaseSelector):
         features in a correlated group and retains the feature with the highest
         importance.
 
+        **"corr_with_target"**: keeps the feature from the correlated group that has the
+        highest correlation with the target variable. The same correlation method
+        defined in the `method` parameter is used to calculate the correlation between
+        the features and the target.
+
     {estimator}
 
     {scoring}
@@ -229,10 +236,12 @@ def __init__(
             "cardinality",
             "variance",
             "model_performance",
+            "corr_with_target",
         ]:
             raise ValueError(
                 "selection_method takes only values 'missing_values', 'cardinality', "
-                f"'variance' or 'model_performance'. Got {selection_method} instead."
+                "'variance', 'model_performance' or 'corr_with_target'. "
+                f"Got {selection_method} instead."
             )
 
         if selection_method == "model_performance" and estimator is None:
@@ -271,7 +280,8 @@ def fit(self, X: pd.DataFrame, y: pd.Series = None):
             The training dataset.
 
         y: pandas series. Default = None
-            y is needed if selection_method == 'model_performance'.
+            y is needed if selection_method == 'model_performance' or
+                'corr_with_target'.
         """
 
         # check input dataframe
@@ -289,9 +299,11 @@ def fit(self, X: pd.DataFrame, y: pd.Series = None):
             _check_contains_na(X, self.variables_)
             _check_contains_inf(X, self.variables_)
 
-        if self.selection_method == "model_performance" and y is None:
+        if (
+            self.selection_method in ["model_performance", "corr_with_target"]
+        ) and y is None:
             raise ValueError(
-                "When `selection_method = 'model_performance'` y is needed to "
+                f"When `selection_method = '{self.selection_method}'` y is needed to "
                 "fit the transformer."
             )
 
@@ -314,6 +326,15 @@ def fit(self, X: pd.DataFrame, y: pd.Series = None):
                 .sort_values(ascending=False)
                 .index.to_list()
             )
+        elif self.selection_method == "corr_with_target":
+            y = check_y(y)
+            features = (
+                X[self.variables_]
+                .corrwith(y, method=self.method)
+                .abs()
+                .sort_values(ascending=False)
+                .index.to_list()
+            )
         else:
             features = sorted(self.variables_)
 
diff --git a/tests/test_selection/test_smart_correlation_selection.py b/tests/test_selection/test_smart_correlation_selection.py
index c7a3254e7..d40f7970b 100644
--- a/tests/test_selection/test_smart_correlation_selection.py
+++ b/tests/test_selection/test_smart_correlation_selection.py
@@ -117,7 +117,7 @@ def test_raises_error_when_threshold_not_permitted(_threshold):
 def test_raises_error_when_selection_method_not_permitted(_method):
     msg = (
         "selection_method takes only values 'missing_values', 'cardinality', "
-        f"'variance' or 'model_performance'. Got {_method} instead."
+        f"'variance', 'model_performance' or 'corr_with_target'. Got {_method} instead."
     )
     with pytest.raises(ValueError) as record:
         SmartCorrelatedSelection(selection_method=_method)
@@ -252,6 +252,21 @@ def test_error_if_select_model_performance_and_y_is_none(df_single):
     assert record.value.args[0] == msg
 
 
+def test_error_if_select_corr_with_target_and_y_is_none(df_single):
+    X, _ = df_single
+
+    transformer = SmartCorrelatedSelection(
+        selection_method="corr_with_target",
+    )
+    msg = (
+        "When `selection_method = 'corr_with_target'` y is needed to fit "
+        "the transformer."
+    )
+    with pytest.raises(ValueError) as record:
+        transformer.fit(X)
+    assert record.value.args[0] == msg
+
+
 def test_selection_method_variance(df_var_car):
     X = df_var_car
 
@@ -434,3 +449,27 @@ def test_smart_correlation_selection_with_groups(df_test_with_groups):
     X_tr = transformer.fit_transform(X, y)
 
     pd.testing.assert_frame_equal(X_tr_expected, X_tr)
+
+
+def test_corr_with_target_single_corr_group(df_single):
+    X, y = df_single
+
+    transformer = SmartCorrelatedSelection(
+        variables=None,
+        method="pearson",
+        threshold=0.8,
+        missing_values="raise",
+        selection_method="corr_with_target",
+    )
+
+    Xt = transformer.fit_transform(X, y)
+
+    # expected result
+    df = X[["var_0", "var_2", "var_3", "var_5"]].copy()
+
+    # test fit attrs
+    assert transformer.correlated_feature_sets_ == [{"var_1", "var_2", "var_4"}]
+    assert transformer.features_to_drop_ == ['var_4', 'var_1']
+    assert transformer.correlated_feature_dict_ == {"var_2": {"var_1", "var_4"}}
+    # test transform output
+    pd.testing.assert_frame_equal(Xt, df)