With a performant surrogate model at our disposal, we turned to the in-house generated HAT dataset. In Figure , the distribution of the computed activation and reaction energies, as well as the correlation between both quantities, is presented. Remarkably, activation and reaction energies only correlate moderately (R 2 = 0.71), indicating that the Bell-Evans-Polanyi principle is not sufficient to fully explain the trends in the activation energies, and that building a predictive model including multiple descriptors is warranted for this dataset. Various baseline models, not making use of the informative representation predicted by our surrogate GNN, were considered as baselines. Due to the relatively small size and inherent diversity present in the constructed HAT dataset, all of these model architectures perform remarkably poorly. RF, KNN and XGBoost models taking structural fingerprints, i.e., differential reaction fingerprints (DRFP), as input, reach a mean absolute error (MAE) of 5.2-5.3 kcal/mol (corresponding to R 2 around 0) in 10-fold crossvalidation. This is barely any better than the standard deviation (7 kcal/mol), i.e., the hypothetical accuracy that would be obtained for a model that always predicts the mean value for the dataset. The GNN architectures tested -a Weisfeiler-Lehmann (WL) one and Chemprop -perform somewhat better, but the MAE still amounts to ∼2.9-3.5 kcal/mol (R 2 ∼ 0.65). The geometrybased kernel ridge regression model, based on the recently introduced Bond-Based Reaction Representation (B 2 R 2 l ) reaches a similar performance (MAE = 3.55 kcal/mol). The results quoted above are in great contrast to the results obtained when the predicted informative representation is used as the input for the reactivity model. Now, the MAEs amount to 1.9-2.3 kcal/mol (cf. Table ) and the corresponding R 2 scores amount to 0.80-0.84. There is still some non-linearity present in the relationship between the representation used and the activation energies but only to a limited extent: our best non-linear model architecture, the FFNN, outperforms a multivariate linear regression model by only 0.30 and 0.36 kcal/mol, in terms of MAE and RMSE, respectively (ensembling 4 FFNNs, the error can be reduced further by 0.02 and 0.03 kcal/mol respectively). In line with this observation, we also observe that fairly few data points are needed to reach this accuracy; already with 800 training points, the final accuracy is approached within 0.1 kcal/mol for most of the machine learning model architectures (Figure ). Reducing the training set size, the multivariate linear model takes over as the most accurate model when fewer than 400 training points are considered. In Fig. , a schematic overview of the impact of the individual descriptors of the (predicted) informative representation on the accuracy of the multivariate linear model is provided. Our analysis indicates that each descriptor has at least some predictive power, i.e., they all correlate to some extent with the activation energies, so that the prediction accuracy for the corresponding univariate linear regression models exceeds the standard deviation across the dataset by a non-negligble margin. As could have been expected from the Bell-Evans-Polanyi analysis in Fig. , the (relaxed) BDFE values correlate extremely well; a linear regression model based exclusively on this (predicted) descriptor reaches an MAE of 2.84 kcal/mol, which is not dramatically worse than the multivariate model based on all (predicted) descriptors. The descriptor that is most complementary to the BDFE, i.e., the descriptor whose inclusion in the linear model reduces the prediction errors the most, is the atomic charge (q), closely followed by the frozen bond dissociation energy (BDE f r ). On their own, the spin density (ρ) values on the radical centers of reactants and products, also have significant predictive power, but they do not improve the model accuracy when the BDFE is already considered as an input descriptor. Note that this is in line with the point of view discussed above that changes in delocalization are fully encoded in the thermodynamic driving force: the information encoded in the spin densities overlaps with the information gained from consideration of the BDFE values. Remarkably, and in contrast to the results obtained for the smaller subsets of chemical space considered as part of the applications (vide infra), the inclusion of V bur does not improve the prediction accuracy on this dataset.