Applicability of the predicted templates. The accuracy of the expansion network only reflects the network's ability to match one product to one template. In practice, when the network is applied to retrosynthetic route planning, the top-50 templates (or a cumulative probability of 0.995) are typically applied to the target product molecule . However, even a highly accurate model is not guaranteed to recommend applicable templates. This problem gives rise to high failure rates for the proposed templates due to the template's inability to match a substructure in the target for which it is was predicted. Table shows the average number of templates it takes to reach cumulative probability above 0.995, and the number of those that actually can be applied to the target molecule. Here we notice differences between the ECFP recommender model and the GCN (RDKit) model. The most notable is the average number of templates that needed to reach a probability of 0.995 is 42.3 for the ECFP model compared to 31.9 for the GCN (RDKit) model. Which could indicate that the GCN (RDKit) model is slightly more certain about the top-1 prediction compared to the ECFP model, and therefore does not need many extra templates to reach a cumulative probability of 0.995. This is confirmed by comparing the average top-1 probability for the GCN (RDKit) and ECFP models which is 0.69 and 0.63, respectively. Because we include less templates we also see a slight decrease in the number of applicable templates from 16.3 to 12.7. However, the success rate of applicable templates in the GCN (RDKit) and ECFP models is approximately 40% in both cases. By comparing the GCN (RDKit) and GCN (RDKit + QM) we see that the average number of templates needed to reach a cumulative probability of 0.995 and the number of applicable templates is practically unaffected by including our chosen QM properties.