It is plausible that a condition fingerprint could be catenated to the reaction fingerprint to predict how E a would be modulated by environment, but to our knowledge the requisite data for a convincing attempt at this does not yet exist. Lastly, our current experiments found marginal advantage from using complementary information sources like ∆H r during training and prediction in the large data limit. More advantages might become apparent for datasets possessing a broader range of reaction mechanisms, datasets with lower irreducible error for the G2Ea task, or by using auxiliary information sources beyond ∆H r . These and adjacent opportunities suggest that the field is far from determining the ultimate performance of G2Ea models.