Kneiding et al. utilized their graph-based dataset (tmQMg) to perform different machine learning tasks using graph convolutional neural networks (GCNNs) . Since SMILES strings represent a 2D molecular graph, these can readily be used to create graphs for GCNNs. Furthermore, since the SMILES can be read into RDKit mol objects, the nodes and edges can be featurized using RDKit and RDKit offers several fingerprint featurization schemes. Molecular fingerprint featurizations such as ECFP4 represent a standard withing machine learning for organic chemistry and often performs well for smaller datasets . We demonstrate the performance of these RDKit-based graphs as well as ECFP4 and compare to the DFT-NBO based graphs from Kneiding et al. For a 3D property such as the dipole moment, we find that the models fed with QM and 3D informed graphs perform significantly better than the models based solely on information available through SMILES which are 2D by nature. However, for the HOMO-LUMO gap and polarizability the RDKit-based graphs perform close to on par with the QM/3D informed graphs.