diff --git a/equinox/nn/composed.py b/equinox/nn/composed.py
index 192117c3..2c4ad497 100644
--- a/equinox/nn/composed.py
+++ b/equinox/nn/composed.py
@@ -13,7 +13,10 @@
 
 import jax.nn as jnn
 import jax.random as jrandom
+import jax.numpy as jnp
 from jaxtyping import Array
+from jax import lax
+from jax.tree_util import tree_flatten
 
 from ..custom_types import PRNGKey
 from ..module import Module, static_field
@@ -101,6 +104,35 @@ def __init__(
         self.activation = activation
         self.final_activation = final_activation
 
+    def _scan_hidden_layers(self, x):
+        def step(inp, layer_weights):
+            weight = layer_weights[:, :-1]
+            bias = layer_weights[:, -1].T
+            layer = self.layers[1]._tree_unflatten(
+                [
+                    ["weight", "bias"],
+                    ["in_features", "out_features", "use_bias"],
+                    [
+                        self.layers[1].in_features,
+                        self.layers[1].out_features,
+                        self.layers[1].use_bias,
+                    ],
+                ],
+                [weight, bias],
+            )
+            inp = self.activation(layer(inp))
+            return inp, None
+
+        flattened_layers, _ = tree_flatten(self.layers[1:-1])
+        concatenated_weight_bias = [
+            jnp.concatenate([weight, bias.reshape(-1, 1)], axis=1)
+            for weight, bias in zip(flattened_layers[::2], flattened_layers[1::2])
+        ]
+        stacked_weights = jnp.stack(concatenated_weight_bias)
+
+        x, _ = lax.scan(step, x, stacked_weights)
+        return x
+
     def __call__(self, x: Array, *, key: Optional[PRNGKey] = None) -> Array:
         """**Arguments:**
 
@@ -113,9 +145,14 @@ def __call__(self, x: Array, *, key: Optional[PRNGKey] = None) -> Array:
 
         A JAX array with shape `(out_size,)`. (Or shape `()` if `out_size="scalar"`.)
         """
-        for layer in self.layers[:-1]:
-            x = layer(x)
+
+        if len(self.layers) > 1:
+            x = self.layers[0](x)
             x = self.activation(x)
+
+        if len(self.layers) > 2:
+            x = self._scan_hidden_layers(x)
+
         x = self.layers[-1](x)
         x = self.final_activation(x)
         return x