The learning curves for these case studies also illustrate the limited transferability of the learned reaction representation to out-of-distribution prediction (Figs. ). These learning curves show the performance of the G→ E a EGAT and Chemprop architectures on the validation split as they are trained. A normalized x-axis is used because the Chemprop and EGAT architectures train over differing numbers of epochs. The within distribution validation curves look typical, with an asymptotic approach to a saturation value that is near the testing set performance shown in Figure . However, the out-of-distribution samples in the validation split show remarkably little reduction in error throughout the training. In some of the cases, a marginal initial reduction occurs that can be interpreted as the models learning generic reaction fingerprint features relevant to all reactions, but these curves still plateau at much larger values. Additional case studies were performed where models that were pretrained on one reaction class were then retrained on an excluded reaction class (Fig. ). In all cases, these models show catastrophic forgetfullness after a single training epoch in predicting validation reactions from their original training class.