The use of machine learning to link molecular structures to properties is well studied. We choose to evaluate two techniques from the literature due to their simplicity: group contribution with linear regression and message-passing neural networks (MPNNs). Both methods start by labeling atoms in the molecule with type information, such as by element or by functional group, but differ significantly in how these labels are mapped to functional properties.