Finally, we demonstrate the practical usefulness of the developed representation by considering various real-world applications. HAT reactivity plays a central role in various sub-fields of chemistry. For example, HAT reactions constitute the starting point of many oxidation pathways in polymer, atmospheric and combustion chemistry. Additionally, they are key transformations giving rise to the breakdown of active pharmaceutical ingredients, so their kinetic and thermodynamic properties facilitate the prediction of the stability of these compounds, as well as the formation of potentially hazardous metabolites. Finally, HAT reactions also play a central role in various common synthesis strategies, e.g. in several flavors of C-H functionalization and photocatalysis, and a lot of effort has been devoted to gaining insights into the factors that affect their rates and yields. As a result, several small datasets have been produced for this reaction class in recent years, each with distinct reaction scopes and conditions, depending on the specific application for which they were designed. Because of their heterogeneity, these datasets cannot be combined easily. As will be demonstrated below, with our extremely data-efficient model architectures, we are able to meaningfully learn from these small datasets, and generate accurate predictions across the chemical spaces associated with the respective application domains, at both an unprecedented speed and an extremely low computational cost.