We compared the predictive performance of models trained on each reaction dataset, and combinations thereof, on 1,731 compounds from 41 virtual libraries at AstraZeneca and the top 125 small molecule therapies of 2018 (Figure ). The models, regardless of reaction dataset, consistently over-estimate the number of compounds that can be synthesized in the case of the virtual libraries, and under-estimate with regards to the top 125 small molecule therapies. For both cases, the average number of steps taken to synthesize a molecule is 4, however the average time taken to solve each molecule varies considerably with the dataset size (Figure ). The smaller datasets are faster at finding routes to a given compound (< 4 seconds) owing to a smaller search space in comparison to the larger search spaces associated with the larger datasets (Pistachio and Reaxys). The simple architecture used is not able to handle the large search space and is biased towards frequently occurring reactions, which are augmented by the additional data in the larger sets. In the case of the top pharmaceutical compounds, the lower predictive performance may arise from more sophisticated ring systems, and natural product like structures upon which the final compound is based. Reactions of this nature are not prioritized by the network as they are infrequent, thus become difficult to separate from the noise.