This study has revisited the chemical graph to activation energy (G2Ea) prediction problem using an edge-featured graph attention (EGAT) architecture. This was motivated by the recent development of the relatively large (∼ 176k) RGD1 organic chemistry reaction dataset, which enabled benchmarking across a broad swathe of reaction types and interrogation of out-of-distribution performance using case-studies with partial training on subsets of RGD1. Another motivation was to contribute an additional open-source architecture for other research groups to experiment with on their own. To the credit of the Chemprop developers, they have opened up their code for adaptation and comparison. Expanding the pool of publicly available models is critical to resolve where fundamental obstacles exist. The summary observations from these experiments are that it is relatively straightforward for G2Ea models to approach the irreducible error of RGD1-estimated to be ∼ 4 kcal/mol-but that out-of-distribution performance is often worse than naïve models.