Table shows the predictive performance on the test set using four different flavours of the ORDerly-condition benchmark. All models show an improvement over the frequency informed baseline. The performance of the labeling datasets at first appears to be better than those that use our custom logic to extract reaction components from the reaction string. However, as shown in Figure , many of the reactions in datasets where we trust the labeling in ORD have more than three reactants, while most reactions in organic chemistry only have two reactants. Upon manual inspection, we found that many agents were mislabeled as reactants and, therefore, the prediction problem was made significantly easier by only requiring a single catalyst to be predicted (see Appendix E). In contrast, our custom cleaning pipeline that defines components using the reaction string avoided contamination of the desired prediction targets (i.e., the agents) in the inputs, and therefore, better represents the downstream application of reaction condition prediction models. This insight is confirmed in Table ; there are fewer unique solvents and agents and a higher density of null components when using the ORD labeling instead of the reaction string indicating that many components might be mislabeled as reactants. This discrepancy demonstrates that naive creation of datasets based on ORD can lead to inflated performance metrics.