The predictive performance on NiCOlit turns out to be far better than the one reported on the highly heterogeneous USPTO dataset (R 2 < 0.2) 7 and on data extracted from AstraZeneca's Electronic Lab Notebooks (R 2 < 0.3) . This shows the potential of machine learning applied to published reaction data. Performances remain nonetheless lower than reported on the HTE data (R 2 > 0.8). An explanation could be the highly biased structure of published reaction data described in Fig. , while HTE data cover a narrow and homogeneous chemical space (Fig. and) and are devoid of experimental and reporting bias . Unlike published reaction data, HTE systematically reports yields for all reactions including low yields, and is comprised of reactions performed in the same experimental settings. This makes them a perfect case study for statistical learning compared to NiCOlit, at the cost of exploring a narrower chemical space. The rest of the manuscript focuses on the results obtained with the DFT model.