Furthermore, the performance is highly variable according to coupling partner or substrate categories. The most straightforward explanation for this behavior is the disparity between the number of reactions documented for each coupling partner category. The models exhibit poor or modest performances for coupling partners with less than 70 reactions reported. Adequate coverage of chemical space is crucial for building ML models . Those results show that a dataset of a much smaller size than NiCOlit can be used to build a predictive model, provided that all reactions belong to the same coupling partner or substrate category (Fig. ). This study also shed light on the approximate number of reactions needed to reach satisfying predictive performance (roughly one hundred reactions). We compared the performances on the restricted sets with between 70-500 reactions and retrieved comparable performances (R 2 ≈ 0.5) than what was obtained on Buchwald HTE data with a similar number of training points (R 2 = 0.59 for 150 data points) . Interestingly, good performances can be attained in low-data regimes by injecting domain specific knowledge. Furthermore, for similar predictive performances, most accessible reaction spaces (last column, Table ) are larger than their HTE counterparts.