Here, we will demonstrate that such a data repurposing strategy can be readily applied to efficiently learn activation energies for hydrogen atom transfer (HAT) reactions. Starting from a generic description of HAT reactivity within the Valence Bond perspective, we determine a set of qualitative characteristics that can be expected to capture the main trends for this type of reactivity. Based on this analysis, we identify the recently released dataset of QM properties for 40,000 closed-shell organic molecules and 200,000 radical analogs by Paton and co-workers, BDE-db, as a useful source of informative descriptors and build a surrogate model able to infer these descriptors on the fly across a broad chemical reaction space based on a simple SMILES string input. The QM representation emerging from this surrogate model is subsequently demonstrated to outperform traditional structurebased representations for various machine learning model architectures on a challenging, in-house generated dataset of diverse hydrogen abstraction reactions by a large margin. Most importantly, models based on the developed surrogate QM representation are extremely data efficient, approaching convergence for training dataset sizes of only a couple hundred points.