Graph-based neural-network models have become notorious in many contexts for overfitting and poor out-of-distribution performance. Although the models trained on RGD1 show excellent testing performance on unseen reactions, this is a large dataset and reactions typically involve a small number of bond changes and conserved mechanisms. This means that even if the testing set involves unseen reactions in terms of reactants or products, it is not expected to necessarily present novel reactivity (e.g., in terms of new types of bonds being broken and formed) that is not seen elsewhere in the training data.