Feature Engineering. Previous work has shown that removing descriptors can have a positive impact on model performance for learned node embeddings. In some cases, certain descriptors may prove redundant or counterproductive to learning. For all features used in this study, we perform ablations to identify such behavior. Tables show all the encodings considered in this study and the final "pruned" set of descriptors that maximize model performance. For descriptors whose removal improved model performance, we propose possible explanations for why they are detrimental to model learning (see Supplementary Tables ). For example, ablating the pH of the organic phase improved model performance. This may be because changes in organic phase pH may not have sufficient time to induce changes in chromatographic interactions; most compounds will have eluted before the organic phase becomes dominant (>50%, see Figure ). For HILIC models, we found that including the concentration of the additives that are present in the mobile phase instead of simply signifying their presence with one hot encoding had a large, positive effect on performancebut this same behavior was not observed for RP predictions. This improvement is likely because the relative concentration of additives (e.g., acidic or basic buffers) will influence the extent of hydrogen bonding occurring between analytes and the HILIC chromatography stationary phases. For node (i.e., atom) features, we found that removal of atomic mass and formal charge improved model performance. Atomic mass is likely redundant for RT prediction, since this information can be inferred from atomic number and the associated scaling factor is likely less important in this context than it might be in others (e.g., IR frequency predictions). Furthermore, the inclusion of partial charge, which encodes effects that are not well-described by the formal charge (e.g., resonance effects), likely makes the formal charge descriptor redundant.