In the past few years several groups have shown the feasibility of predicting activation energies from only the reactant and product graphs (Fig. ). Early examples focused on summarizing changes between reactants and products using expert-generated features and molecular fingerprints. With the advent of larger reaction datasets, several standard practices have been identified, including the necessity to avoid including reverse reactions in testing datasets (i.e., having an example in the testing set that was seen during training with the reactant and product switched), the potential usefulness of the heat of reaction (∆H r ) as an input feature, and the advantage of learnable reaction fingerprints over pre-defined expert fingerprints. In paral- Comparisons across datasets and predictions on unseen reactions will be out-of-distribution if such factors are not consistently sampled. Despite many practical demonstrations of the graph-to-activation-energy (G2Ea) concept, sev-eral challenges persist that limit the usefulness of these models as drop-in replacements for quantumchemistry based TS searches. One challenge is that the scarcity of large reaction datasets has limited convincing out-of-distribution tests of the transferability of G2Ea models. Under the assumption that reaction mechanisms are conserved, G2Ea models should be capable of extrapolating to unseen substrates involved in classes of reactions that have been directly trained on, but this remains a largely untested hypothesis.