Once a model architecture and representation for molecules (2D graph, 3D coordinates, fingerprint, string) has been chosen, there are still many options for what input features to use for the encoding of molecules within that representation. The inclusion of features relevant to the target property can make a significant difference in the ability of the model to learn the property function. Errors due to the choice of input features are a form of model bias. Fig. depicts model performances for the QM9 targets enthalpy and HOMO-LUMO gap for different model inputs. First, we skipped the message passing step and used a Morgan fingerprint 58 of size 10, 100 or 1000 as input to a feed-forward neural network. Second, we modified the default d-MPNN representation of the molecular graph not to discern between carbon and nitrogen (labeled 'd-MPNN C,N') or carbon, nitrogen, and oxygen (labeled 'd-MPNN C,N,O') to artificially create bad features. For both targets, d-MPNNs outperform fingerprints, where smaller fingerprints lead to even worse performance. Bad features in the d-MPNN again decrease model performance. With increasing data, models with corrupted features can regain performance, since bias from featurization can be a reducible error source if the missing information can be learned, e.g. from the structure of the rest of the molecule. Thus, finding optimal features is less important for large datasets, but essential for medium-sized datasets. However, error from featurization can also be irreducible if the model loses important information that it cannot learn or infer. This is the case for the fingerprint of size 10 in Fig. , which is too small to faithfully represent the diversity of molecules present in the dataset. Despite these insights being rather expected, we find that often not enough attention is paid to featurization when building new models. For example, targets like the enthalpy might require additional features such as ring sizes, which are not default in e.g. the implementation of d-MPNNs we utilized in this work. In fact, adding a one-hot encoded ring size to atom and bond features increases the performance of our d-MPNN from mean absolute errors of 0.30 to 0.19 eV for N=100,000. We also recently trained d-MPNNs to predict solute parameters, solvation free energies, enthalpies or solubilities, where we found atom features specific to solvation such as the presence of H-bond donors or acceptors to be key to good model performance. For the prediction of molecular UV-Vis absorption peaks, we furthermore found that the inclusion of a model prediction trained on low-fidelity data as an additional custom molecular feature within a high-fidelity model can be beneficial.