We investigated how a selection of semi-empirical QM properties would influence the accuracy of the expansion policy networks used for retrosynthetic planning. We used graph representations to train graph neural networks to incorporate the atomic QM properties into a meaningful molecular descriptor. We trained the neural graph networks with and without QM properties and found that the selected semi-empirical QM properties did not affect the accuracy or template applicability. It is important to highlight that we only tested a few QM properties and did not perform an exhaustive test of QM properties. Other properties could potentially yield a different conclusion as illustrated by Stuyver et al. . By changing from the ECFP representation to a graph representation, the top-5, top-10, and top-50 accuracies increased by 5.5-8.5% points. When switching to a graph representation, we observed that the number of templates needed to reach a cumulative probability of 0.995 decreased from 42.2 to 31.3. This decrease resulted in a slight reduction in the number of applicable templates (16.3 to 12.7), which made us question the contradictory way we train and apply the expansion policy network. When the expansion policy network is trained, we target a one-to-one mapping between the target molecule and template documented in the training set. However, when the network is applied for retrosynthesis prediction, we expect the network to return multiple applicable templates for the target molecule. We also tested if oversampling/undersampling can increase the accuracy of rare templates without decreasing the accuracy of the frequently used ones. These relatively simple techniques result in a substantial top-1 accuracy increase of approximately 20% for templates represented five times or less in the USPTO reaction dataset. We tested three different ways of oversampling/undersampling and found that not all methods perform equally well. If the templates are sampled evenly, you see a severe decrease in accuracy for templates used more than 100 times. But if you only oversample templates observed 50 times or less, we do not observe the same severe decrease in accuracy for frequently used templates.