The poor data-efficiency of generic machine learning models poses a major challenge to their application in chemical reactivity studies, since for most specialized predictive tasks in this domain, only limited training data is available. In this work, we have explored a strategy to make machine learning workflows for reactivity prediction significantly less data-hungry, by making use of an intermediate informative reaction representation. This informative representation was constructed based on a VB analysis for generic HAT reactions. A surrogate model, able to predict (most of) the identified quantum chemical informative descriptors was subsequently built based on a repurposed, publicly available dataset of descriptors and geometries of organic radicals. Combining the surrogate model with a secondary reactivity model, we achieved unequivocally superior performance on a medium-sized, in-house generated, dataset of DFT computed re- action profiles of diverse HAT reactions, compared to any other machine learning approach we tried. Because of the extreme data efficiency of our descriptor-augmented models, we were able to demonstrate that they can be fine-tuned and applied to small datasets across various reaction conditions, settings and application domains, ranging from regular (liquid phase) synthesis, over metabolism and drug design, to atmospheric chemistry.