diff --git a/emukit/model_wrappers/gpy_model_wrappers.py b/emukit/model_wrappers/gpy_model_wrappers.py
index da38098a..a3bbb82b 100644
--- a/emukit/model_wrappers/gpy_model_wrappers.py
+++ b/emukit/model_wrappers/gpy_model_wrappers.py
@@ -80,7 +80,7 @@ def calculate_variance_reduction(self, x_train_new: np.ndarray, x_test: np.ndarr
         """
         Computes the variance reduction at x_test, if a new point at x_train_new is acquired
         """
-        covariance = self.model.posterior_covariance_between_points(x_train_new, x_test)
+        covariance = self.model.posterior_covariance_between_points(x_train_new, x_test, include_likelihood=False)
         variance_prediction = self.model.predict(x_train_new)[1]
         return covariance**2 / variance_prediction
 
@@ -317,7 +317,7 @@ def get_covariance_between_points(self, X1: np.ndarray, X2: np.ndarray) -> np.nd
                    argument to the posterior covariance function.
         :return: An array of shape n_points x 1 of posterior covariances between X1 and X2
         """
-        return self.gpy_model.posterior_covariance_between_points(X1, X2)
+        return self.gpy_model.posterior_covariance_between_points(X1, X2, include_likelihood=False)
 
     def generate_hyperparameters_samples(self, n_samples = 10, n_burnin = 5, subsample_interval  = 1,
                                          step_size = 1e-1, leapfrog_steps = 1) -> None: