The latter results confirm and emphasize the lack of predictive power of ML for reaction condition prediction that are not directly represented within the training data, in low data regimes which are of particular relevance to bench chemists. Within the LoGo CV experiment, even if a diverse chemical space is captured in the training data, the average prediction performance for the held-out test data was limited. The preliminary assessment of these investigation allows for benchmarking of combinations of input representations and surrogate models by showing the most appropriate strategy for similar sized datasets generated by the chemical community. We believe that the performance of extrapolative predictions may be better with larger datasets and, also, may vary depending on the reaction mechanism itself since this affects the learning of structure-reactivity relations ships by ML models.