With respect to the comparison of MCTS and Retro*, there are some implementation details to consider. In the paper introducing Retro*, it was argued that Retro* was advantageous over MCTS because it made fewer calls to the one-step model . However, in our implementation of MCTS, the template application is deferred until it is necessary, heavily reducing the number of template applications. Thus for the template-based one-step models used herein, we observe that MCTS and Retro* are approximately equally fast to reach convergence in the number of solved routes. However, it is becoming more and more popular with template-free one-step retrosynthesis models, e.g. transformerlike architectures. Such machine learning (ML) models are much slower than the simple feed-forward architecture used in the template-based model. Therefore, using a more expensive ML model or template-free methods that do not need to apply a template would tip the speed advantage to the favor of Retro*.