Over the past few years, significant progress has been made in computer-aided synthesis planning (CASP) leading to highly promising tools for accelerating the synthesis of chemical compounds and providing valuable feedback to generative models on the synthesizability of suggested molecules. However, one of the major challenges for CASP tools is their ability to account for chemical selectivity. 42,43 A recent paper even emphasizes the need for incorporating expert knowledge (i.e. reaction rules) into data-driven CASP tools to address the selectivity problems. Although different reaction conditions can alter the chemical selectivity and such "condition matching would require an extensive study by expert chemists and would likely only be applicable for extremely simple reaction types". As an alternative to reaction rules, we suggest using MCA and MAA values to assess the relative reactivity between reac-tion sites. This approach is demonstrated in Figure for reaction examples where predicting the selectivity poses a challenge for data-driven methods. , where predicting the selectivity can be a challenge for data-driven reaction prediction models. Note that only the recorded products are shown. The values in the parentheses are the estimated errors used to detect outliers, where running the QM-based workflow would be recommended. However, all values are below the predefined cutoffs of 25 and 30 kJ/mol for MCA and MAA, respectively.