The MCTS algorithm consists of four phases: selection, expansion, rollout, and update. In the expansion phase, new nodes are added to the search tree. The new nodes are added based on the expansion procedure illustrated in Figure . The target molecule is parsed through an expansion policy network, which is a neural network trained to recommend retrosynthetic templates. Typically, the search space of the MCTS is limited by only including the 50 most probable templates (or the number of templates it takes to reach a cumulative probability of 0.995) .The reaction databases used to train the expansion policy network are often very imbalanced, i.e. some templates are used frequently, while many are rarely used. In the US Patent Office reaction database (USPTO) curated by , 10% of all recorded reactions use transformations that is only reported five times or less . However, the infrequently used transformations account for roughly 60% of all unique templates found in the database. In the other end, 50% of the reactions use templates that are reported more than 100 times but the frequent transformations only account for 2% of the unique templates. The imbalanced nature of the reactions databases results in expansion policy networks biased towards frequently used templates. For example, the expansion policy network published with AIZynthFinder has a top-1 accuracy of 65.7% for reactions that use templates that occur more than 100 times in the USPTO database, but only a top-1 accuracy of 34.8% for reactions that use templates recorded five times or less. In essence, the expansion policy networks will perform worse for less frequently used templates. There have been proposed multiple ideas to address the low-data issue. Some solutions rely on template applicability models and knowledge transfer to increase accuracy in the low-data domain . Others use structural information about the templates to allow the models generalizing information across template classes .