Next, we turn to the experimental dataset of 45 HAT activation energies, assembled by Bietti and co-workers. Our models, trained on the alkoxy radical dataset, predict the (rescaled) activation energies in this experimental set with acceptable accuracy (∼ 1 kcal/mol; cf. Section S5.2 of the Supporting Information), correctly identifying all of the reaction barriers to be low. However, the coefficients of determination, i.e., the extent to which fine-grained trends across these 45 reactions are recovered, are remarkably poor (R 2 ∼ 0.35). The main reason for this failure to recover the reactivity trends is the distinct data distribution, i.e., the activation energies in this specific experimental dataset are "outliers" with respect to the reactions present in Ma et al.'s dataset, and -to a lesser extent -the fairly low spread in the targets (the standard deviation amounts to only 1.5 kcal/mol). This conjecture can be confirmed by retraining our models on the 45 data points by Bietti et al.: in 10-fold cross-validation (i.e., when only 36 in-distribution training points are considered), a slightly better R 2 = 0.47 is now reached for our best model, the ensembled FFNN with transfer learning.