For the HOMO-LUMO gap a larger discrepancy is apparent between the performance on the training and test sets. Surprisingly, the Gilmer model trained on the SMILES graph data matches the performance of the Gilmer models trained on the significantly more expensive DFT-NBO data. The GCNN also performs much better than the fingerprint models.