Five MCTS variants have previously been explored for retrosynthesis. Modified UCT (mUCT), alters the method to save time on the mandatory 'ergodic' step in the method, referring to the mandatory exploration of all possible moves from a given state before a deeper or more biased exploration. mUCT with dynamic 'C' (mUCT-dc) introduces dynamic tuning of the parameter C in the UCT equation. This method addresses the same problem targeted by the enhancements proposed in our work. However, mUCT-dc focuses on adapting the balance between exploitation and exploration based on how under/over-explored certain areas of the tree are. Polynomial Upper Confidence Trees (PUCT) incorporates a policy network term to the selection phase of MCTS, providing a probability distribution of all possible moves from a given state. The final two enhancements were the use of a value network replacing the traditional Monte-Carlo rollouts, and bootstrapping which utilizes the outcomes from self-play in the training of the value network.