For training sets as small as 200 data points, the resulting "QMaugmented" models reached accuracies of 85% and more on a random data split, outperforming regular GNN analogs by more than 10%. Furthermore, the developed models were extremely fast; individual predictions required 100 ms or less, implying that they could be readily employed in high-throughput screening campaigns. In a follow-up study, Stuyver et al. adapted this model to predict activation energies for competing (gas-phase) E2 and S N 2 reactions and observed a remarkable improvement in accuracy for their GNN model upon QM-augmentation as well, particularly in the data-limited regime. Even when a surrogate model strategy is viable, initial training data for the model still needs to be generated, and in some situations, this may actually be more expensive than the explicit calculation of QM descriptors at inference time. For example, in their proof of principle study, Guan et al. generated a dataset of selected QM properties for 130k organic molecules, extracted from the ChEMBL and Pistachio 24 databases.