Removing rare molecules can increase the signal to noise ratio in a dataset by removing outliers. In this work, we investigated two different strategies for filtering spectator molecules based on their frequency: deleting reactions with rare spectator molecules (rare!delete rxn) or keeping the reactions but mapping the rare molecules to an "other" category (rare!"other") (see Figure ). We conducted experiments with both the rare!delete rxn and rare!"other" strategies for the task of condition prediction. The frequency threshold was set at 100 in line with previous research , though the sensitivity of dataset size to frequency threshold was still investigated (see Appendix D.2). Deleting reactions with rare molecules may create a more cohesive dataset by removing outliers, while renaming rare molecules "other" allows more reactions to be kept, offering more training data for the model.