One key difference between AiZynthFinder and 3N-MCTS is the style of the MCTS. 3N-MCTS utilizes 100,000 iterations per molecule, in contrast to 100 used by AiZynthFinder. This disparity highlights the importance of the value network in AiZynthFinder, as the algorithm relies heavily on the network scores to direct the Monte Carlo sampling. Segler et al.'s approach uses optimized neural network architectures and advanced parallel processing, enabling a higher number of iterations by narrowing the search space and focusing on highly probable transformations. In contrast, AiZynthFinder emphasizes robustness and reproducibility, resulting in fewer, more comprehensive iterations per molecule. Therefore, it will be crucial to balance computational expenditure between the value network and MCTS. Our results show that increasing the iteration count is beneficial in solving more molecules, and our enhancements display a large increase in the number of iterations that can be achieved in a given time.