The presented modular approach towards reactivity prediction has the potential to significantly expand the scope of machine learning workflows for focused reactivity predictions, as long as the reactivity type is qualitatively understood and can be modeled accurately, and high-quality descriptor datasets are available for surrogate model training. In this context, it is crucial to emphasize the necessity for careful curation of these property datasets, ensuring that the chosen descriptors are pertinent, i.e., relevant, to a diverse array of reaction types.