In our proof-of-concept tests (Table ) on 1,500 heavy molecules, eUCT solved the same number of molecules as UCT, and found ~0.2 more routes per solved molecule. The low percentage of solved molecules across all methods is due to the complexity of the molecules. eUCT is predicated on variation in success probability between explorative nodes and exploitative nodes in the search tree. If there is a lower probability of reaching a successful leaf-node in nodes which have been explored infrequently, which is often the case in MCTS, eUCT should reach successful routes faster in 'later' iterations of a search, particularly as they approach the maximum number of iterations. However, AiZynthFinder does not conform to these conditions. The value network provides predictive scores for nodes, regardless of whether they have been visited in the search tree or not. Consequently, if the value network is reliable, there is no difference in the probability of success between deep and shallow nodes because the neural network policy has already predicted the score for each possible disconnection. This could arise from overfitting of the neural network, a speculation that is supported by the repeatability of the results. Repeating a search on the same molecule resulted in near identical solutions found, often found during the same iteration. Despite not solving more molecules, eUCT attained a greater number of successful routes, and consequently a higher average route count per solved molecule. A 6% decrease in computational clock-time was observed, which is a statistically significant improvement (a Wilcoxon signed-rank test gave a p-value of 6.9 × 10 -5 ). Despite the decrease in clock-time, there was little change in the number of iterations required to solve a molecule. Thus, the speed increase is likely due to fewer nodes being visited per iteration.