The average number of successfully applied templates of the top 50 predicted templates for one-step synthesis per compound varies considerably across the reaction datasets examined (Figure ). The model built on a subset of the AstraZeneca ELN appears to be worse than the models built on other reaction datasets by this measure. However, we have found that the number of options the network suggests for one-step synthesis does not impact overall model performance in this case. Thus, as Segler and Waller suggested in a previous study examining training set size, models competitive with those built on larger reaction sets can be obtained with datasets as small as an internal ELN. The subset of the AstraZeneca ELN accounts for 4.5 % of the template library obtained from a combination of all datasets examined, yet is capable of providing sufficient training data to train filter networks and resulting models which are competitive with those of larger proprietary datasets. However, we expect that this is domain specific and reflects that the subet of the AstraZeneca ELN is tailored to the medicinal chemistry domain in comparison to the patent and Reaxys datasets, which are more extensive in their converage (Figure ). This further demonstrates that there is a balance between the type of chemistry covered by the template library set, and the size of the template library. An optimal set would be domain specific, and cover enough examples of sufficient diversity, that the output space would be managable by the filter network. In the current approach we have found that as the dataset size increases, so does the output space of the filter network (Table ). This increases the time taken to train the network, and makes it increasingly difficult for the network to prioritize appropriate reactions as seen when increasing template library size in Figure .