The fact that we, on average, can apply 281 of the 46,695 templates raises an essential question about the training setup. For retrosynthetic prediction, we are not in general interested in training a network that only recreates the one-to-one mapping found in the training set (target molecule to a template). Instead, we want the network to propose several applicable templates, preferably a mix of rare and common templates. However, as training is set up for most data-driven retrosynthetic tools available, the network is, during training, attempting to recreate the one-to-one mapping. This is due to the choice of the loss function, i.e. the cross-entropy loss, which only rewards the network if it succeed in an exact one-to-one mapping . The discrepancy between the training objective and how we are applying the model means, in the extreme case, that we are relying on coincidences that there is more than one applicable template among the top-50 templates. The training setup will force the network to get as close as possible to predict precisely one template with more certainty. During training the network will attempt to make the top-1 probability as close as possible to 1. Thus, we will likely observe that the more accurate (or specific) the model becomes, the fewer templates the model recommend (given the same probability cut-off). Because the model is more confident in its prediction (top-1 probability is closer to 1), we needed fewer templates to reach a cumulative probability of 0.995. This trend is observed when we compare the average templates of the GCN (RDKit) and ECFP recommender model in Table .