diff --git a/gnina/inference.py b/gnina/inference.py
index d543ac9..1ae6e07 100644
--- a/gnina/inference.py
+++ b/gnina/inference.py
@@ -153,6 +153,9 @@ def inference(args):
     # Create model
     model = models.models_dict[(args.model, affinity)](test_loader.dims).to(device)
 
+    # Compile model with TorchScript
+    model = torch.jit.script(model)
+
     # Load checkpoint
     checkpoint = torch.load(args.checkpoint, map_location=device)
     Checkpoint.load_objects(to_load={"model": model}, checkpoint=checkpoint)
diff --git a/gnina/losses.py b/gnina/losses.py
index 71943b4..8a67dfa 100644
--- a/gnina/losses.py
+++ b/gnina/losses.py
@@ -90,7 +90,7 @@ def forward(self, input: Tensor, target: Tensor) -> Tensor:
 
         if self.reduction == "mean":
             reduced_loss = torch.mean(loss)
-        elif self.reduction == "sum":
+        else:  # Assertion in init ensures that reduction is "sum"
             reduced_loss = torch.sum(loss)
 
         return self.scale * reduced_loss
diff --git a/gnina/training.py b/gnina/training.py
index be4f32e..d22858b 100644
--- a/gnina/training.py
+++ b/gnina/training.py
@@ -589,8 +589,8 @@ def training(args):
     model = models_dict[(args.model, affinity)](train_loader.dims).to(device)
     model.apply(weights_and_biases_init)
 
-    # TODO: Compile model into TorchScript
-    # Requires model refactoring to avoid branching based on affinity
+    # Compile model into TorchScript
+    model = torch.jit.script(model)
 
     optimizer = optim.SGD(
         model.parameters(),
@@ -600,13 +600,15 @@ def training(args):
     )
 
     # Define loss functions
-    pose_loss = nn.NLLLoss()
+    pose_loss = torch.jit.script(nn.NLLLoss())
     affinity_loss = (
-        AffinityLoss(
-            delta=args.delta_affinity_loss,
-            penalty=args.penalty_affinity_loss,
-            pseudo_huber=args.pseudo_huber_affinity_loss,
-            scale=args.scale_affinity_loss,
+        torch.jit.script(
+            AffinityLoss(
+                delta=args.delta_affinity_loss,
+                penalty=args.penalty_affinity_loss,
+                pseudo_huber=args.pseudo_huber_affinity_loss,
+                scale=args.scale_affinity_loss,
+            )
         )
         if affinity
         else None