Here, the transferability of G2Ea models has been revisited using the recently developed RGD1 dataset and an adaptation of the graph attention architecture. The motivation for this study was that the size and mechanistic diversity of the RGD1 dataset potentially allows for the training of more data-demanding architectures with better transferability. Using a graph attention architecture we are able to train models that reliably approach the estimated irreducible error of RGD1 and perform well on withheld reactions drawn from the same distribution. Nevertheless, these models show minimal transferability in external testing scenarios, oftentimes performing worse than naïve mean-predicting models. Through several comparative case-studies it is shown that this behavior is shared by models using closely related featurizations, and other contemporary graph to property architectures recently published for use in the G2Ea prediction task.