diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index b209f16..652b5a6 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 1.5.1a1 (WIP)
+Release 1.5.1a2(WIP)
 ---------------------------
 
 Breaking Changes:
@@ -21,6 +21,7 @@ SB3-Contrib
 Bug Fixes:
 ^^^^^^^^^^
 - Fixed saving and loading large policies greater than 2GB (@jkterry1, @ycheng517)
+- Fixed final goal selection strategy that did not sample the final achieved goal (@qgallouedec)
 
 Deprecations:
 ^^^^^^^^^^^^^
diff --git a/stable_baselines3/her/her_replay_buffer.py b/stable_baselines3/her/her_replay_buffer.py
index 9a41477..f61a786 100644
--- a/stable_baselines3/her/her_replay_buffer.py
+++ b/stable_baselines3/her/her_replay_buffer.py
@@ -252,7 +252,7 @@ class HerReplayBuffer(DictReplayBuffer):
         elif self.goal_selection_strategy == GoalSelectionStrategy.FUTURE:
             # replay with random state which comes from the same episode and was observed after current transition
             transitions_indices = np.random.randint(
-                transitions_indices[her_indices] + 1, self.episode_lengths[her_episode_indices]
+                transitions_indices[her_indices], self.episode_lengths[her_episode_indices]
             )
 
         elif self.goal_selection_strategy == GoalSelectionStrategy.EPISODE:
@@ -262,7 +262,7 @@ class HerReplayBuffer(DictReplayBuffer):
         else:
             raise ValueError(f"Strategy {self.goal_selection_strategy} for sampling goals not supported!")
 
-        return self._buffer["achieved_goal"][her_episode_indices, transitions_indices]
+        return self._buffer["next_achieved_goal"][her_episode_indices, transitions_indices]
 
     def _sample_transitions(
         self,
@@ -304,14 +304,6 @@ class HerReplayBuffer(DictReplayBuffer):
 
         ep_lengths = self.episode_lengths[episode_indices]
 
-        # Special case when using the "future" goal sampling strategy
-        # we cannot sample all transitions, we have to remove the last timestep
-        if self.goal_selection_strategy == GoalSelectionStrategy.FUTURE:
-            # restrict the sampling domain when ep_lengths > 1
-            # otherwise filter out the indices
-            her_indices = her_indices[ep_lengths[her_indices] > 1]
-            ep_lengths[her_indices] -= 1
-
         if online_sampling:
             # Select which transitions to use
             transitions_indices = np.random.randint(ep_lengths)
diff --git a/stable_baselines3/version.txt b/stable_baselines3/version.txt
index 1110517..1a2eef7 100644
--- a/stable_baselines3/version.txt
+++ b/stable_baselines3/version.txt
@@ -1 +1 @@
-1.5.1a1
+1.5.1a2