diff --git a/mlir/lib/Dialect/Linalg/Transforms/ConvertToDestinationStyle.cpp b/mlir/lib/Dialect/Linalg/Transforms/ConvertToDestinationStyle.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/ConvertToDestinationStyle.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ConvertToDestinationStyle.cpp
@@ -461,18 +461,20 @@
   AnalysisState state(bufferizationOptions);
 
 #ifndef NDEBUG
-  // Ops with nested tensor ops are not supported yet. At the moment, this
-  // function just bufferizes the given op itself, but not its body.
-  op->walk([&](Operation *nestedOp) {
-    if (op == nestedOp)
-      return;
-    if (llvm::any_of(nestedOp->getOperands(),
-                     [](Value v) { return v.getType().isa<TensorType>(); }))
-      llvm_unreachable("ops with nested tensor ops are not supported yet");
-    if (llvm::any_of(nestedOp->getResults(),
-                     [](Value v) { return v.getType().isa<TensorType>(); }))
-      llvm_unreachable("ops with nested tensor ops are not supported yet");
-  });
+  if (!options.bufferizeDestinationOnly) {
+    // Ops with nested tensor ops are not supported yet. At the moment, this
+    // function just bufferizes the given op itself, but not its body.
+    op->walk([&](Operation *nestedOp) {
+      if (op == nestedOp)
+        return;
+      if (llvm::any_of(nestedOp->getOperands(),
+                       [](Value v) { return v.getType().isa<TensorType>(); }))
+        llvm_unreachable("ops with nested tensor ops are not supported yet");
+      if (llvm::any_of(nestedOp->getResults(),
+                       [](Value v) { return v.getType().isa<TensorType>(); }))
+        llvm_unreachable("ops with nested tensor ops are not supported yet");
+    });
+  }
 #endif // NDEBUG
 
   // Gather tensor results.
@@ -509,7 +511,7 @@
     if (!state.bufferizesToMemoryWrite(operand))
       continue;
     if (!isa<RankedTensorType>(operand.get().getType()))
-      return nullptr;
+      continue;
     addOutOfPlaceOperand(&operand);
   }
   // TODO: Support multiple buffers.
diff --git a/mlir/test/Dialect/Linalg/transform-op-bufferize-to-allocation.mlir b/mlir/test/Dialect/Linalg/transform-op-bufferize-to-allocation.mlir
--- a/mlir/test/Dialect/Linalg/transform-op-bufferize-to-allocation.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-bufferize-to-allocation.mlir
@@ -218,3 +218,27 @@
   %0 = transform.structured.match ops{["tensor.insert"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %2, %new = transform.structured.bufferize_to_allocation %0 {memory_space = 4, bufferize_destination_only} : !transform.any_op
 }
+
+// -----
+
+// CHECK-LABEL: func @scf_for_destination(
+//  CHECK-SAME:     %[[t:.*]]: tensor<?x10xindex>
+//       CHECK:   %[[alloc:.*]] = memref.alloc(%{{.*}}) : memref<?x10xindex, 4>
+//       CHECK:   memref.tensor_store %[[t]], %[[alloc]]
+//       CHECK:   %[[t2:.*]] = bufferization.to_tensor %[[alloc]] restrict writable
+//       CHECK:   %[[for:.*]] = scf.for {{.*}} iter_args(%{{.*}} = %[[t2]])
+//       CHECK:   memref.dealloc %[[alloc]]
+//       CHECK:   return %[[for]]
+func.func @scf_for_destination(%t: tensor<?x10xindex>, %lb: index, %ub: index, %step: index) -> tensor<?x10xindex> {
+  %r = scf.for %iv = %lb to %ub step %step iter_args(%a = %t) -> tensor<?x10xindex> {
+    %b = "test.foo"(%a) : (tensor<?x10xindex>) -> (tensor<?x10xindex>)
+    scf.yield %b : tensor<?x10xindex>
+  }
+  return %r : tensor<?x10xindex>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["scf.for"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %2, %new = transform.structured.bufferize_to_allocation %0 {memory_space = 4, bufferize_destination_only} : !transform.any_op
+}