diff --git a/sbi/diagnostics/sbc.py b/sbi/diagnostics/sbc.py
index 776727735..ba01fb0a8 100644
--- a/sbi/diagnostics/sbc.py
+++ b/sbi/diagnostics/sbc.py
@@ -48,8 +48,7 @@ def run_sbc(
         num_workers: number of CPU cores to use in parallel for running
             `num_sbc_samples` inferences.
         show_progress_bar: whether to display a progress over sbc runs.
-        use_batched_sampling: whether to use batched sampling for posterior
-            samples.
+        use_batched_sampling: whether to use batched sampling for posterior samples.
 
     Returns:
         ranks: ranks of the ground truth parameters under the inferred
diff --git a/sbi/diagnostics/tarp.py b/sbi/diagnostics/tarp.py
index 3fe2f1e52..44ff114f3 100644
--- a/sbi/diagnostics/tarp.py
+++ b/sbi/diagnostics/tarp.py
@@ -28,6 +28,7 @@ def run_tarp(
     distance: Callable = l2,
     num_bins: Optional[int] = 30,
     z_score_theta: bool = True,
+    use_batched_sampling: bool = True,
 ) -> Tuple[Tensor, Tensor]:
     """
     Estimates coverage of samples given true values thetas with the TARP method.
@@ -54,6 +55,7 @@ def run_tarp(
         num_bins: number of bins to use for the credibility values.
             If ``None``, then ``num_sims // 10`` bins are used.
         z_score_theta : whether to normalize parameters before coverage test.
+        use_batched_sampling: whether to use batched sampling for posterior samples.
 
     Returns:
         ecp: Expected coverage probability (``ecp``), see equation 4 of the paper
@@ -67,6 +69,7 @@ def run_tarp(
         (num_posterior_samples,),
         num_workers,
         show_progress_bar=show_progress_bar,
+        use_batched_sampling=use_batched_sampling,
     )
     assert posterior_samples.shape == (
         num_posterior_samples,
diff --git a/sbi/utils/diagnostics_utils.py b/sbi/utils/diagnostics_utils.py
index e68e1ab79..d53cd536c 100644
--- a/sbi/utils/diagnostics_utils.py
+++ b/sbi/utils/diagnostics_utils.py
@@ -1,9 +1,12 @@
+import warnings
+
 import torch
 from joblib import Parallel, delayed
 from torch import Tensor
 from tqdm import tqdm
 
 from sbi.inference.posteriors.base_posterior import NeuralPosterior
+from sbi.inference.posteriors.mcmc_posterior import MCMCPosterior
 from sbi.inference.posteriors.vi_posterior import VIPosterior
 from sbi.sbi_types import Shape
 
@@ -29,18 +32,23 @@ def get_posterior_samples_on_batch(
     Returns:
         posterior_samples: of shape (num_samples, batch_size, dim_parameters).
     """
-    batch_size = len(xs)
+    num_xs = len(xs)
 
-    # Try using batched sampling when implemented.
-    try:
-        # has shape (num_samples, batch_size, dim_parameters)
-        if use_batched_sampling:
+    if use_batched_sampling:
+        try:
+            # has shape (num_samples, num_xs, dim_parameters)
             posterior_samples = posterior.sample_batched(
                 sample_shape, x=xs, show_progress_bars=show_progress_bar
             )
-        else:
-            raise NotImplementedError
-    except NotImplementedError:
+        except (NotImplementedError, AssertionError):
+            warnings.warn(
+                "Batched sampling not implemented for this posterior. "
+                "Falling back to non-batched sampling.",
+                stacklevel=2,
+            )
+            use_batched_sampling = False
+
+    if not use_batched_sampling:
         # We need a function with extra training step for new x for VIPosterior.
         def sample_fun(
             posterior: NeuralPosterior, sample_shape: Shape, x: Tensor, seed: int = 0
@@ -51,8 +59,16 @@ def sample_fun(
             torch.manual_seed(seed)
             return posterior.sample(sample_shape, x=x, show_progress_bars=False)
 
+        if isinstance(posterior, (VIPosterior, MCMCPosterior)):
+            warnings.warn(
+                "Using non-batched sampling. Depending on the number of different xs "
+                f"( {num_xs}) and the number of parallel workers {num_workers}, "
+                "this might take a lot of time.",
+                stacklevel=2,
+            )
+
         # Run in parallel with progress bar.
-        seeds = torch.randint(0, 2**32, (batch_size,))
+        seeds = torch.randint(0, 2**32, (num_xs,))
         outputs = list(
             tqdm(
                 Parallel(return_as="generator", n_jobs=num_workers)(
@@ -61,7 +77,7 @@ def sample_fun(
                 ),
                 disable=not show_progress_bar,
                 total=len(xs),
-                desc=f"Sampling {batch_size} times {sample_shape} posterior samples.",
+                desc=f"Sampling {num_xs} times {sample_shape} posterior samples.",
             )
         )  # (batch_size, num_samples, dim_parameters)
         # Transpose to shape convention: (sample_shape, batch_size, dim_parameters)
@@ -70,8 +86,8 @@ def sample_fun(
         ).permute(1, 0, 2)
 
     assert posterior_samples.shape[:2] == sample_shape + (
-        batch_size,
-    ), f"""Expected batched posterior samples of shape {
-        sample_shape + (batch_size,)
-    } got {posterior_samples.shape[:2]}."""
+        num_xs,
+    ), f"""Expected batched posterior samples of shape {sample_shape + (num_xs,)} got {
+        posterior_samples.shape[:2]
+    }."""
     return posterior_samples