In essence, the proposed strategy rests on making the overall machine learning model architecture modular, with a core informative representation prediction part, followed by a fairly linear and extremely data-efficient downstream reactivity model part, that can be easily retrained on/finetuned to the specific application or dataset at hand. Overall, we believe that the presented work provides a blueprint for how -starting from physics/chemistry-inspired mechanistic insights -useful (existing) datasets of QM properties can be identified and leveraged for downstream predictive tasks. Furthermore, it underscores the importance of community efforts to generate and curate QM property datasets -and to include as many informative descriptors as possible in them. At the end of this contribution, we briefly reflect on the transferability of the approach to other reaction types.