Progress in ML is markedly driven by the increasing access to data. Thus, currently available datasets shape the evolution of ML for reaction yield prediction. Despite this, there are very few publicly available and easily operable datasets of chemical reactions with associated yields (Table ). One of those few public datasets is the United State Patent and Trademark Office (USPTO) dataset, that covers a wide range of chemical reactions extracted from patents. USPTO data is extremely diverse and suffers from a selection bias as only successful reactions tend to be reported in patents. ML has shown poor performance predicting yields on this dataset (R 2 < 0.2) . In addition, two sets of High Throughput Experimentation (HTE) data, one of a Suzuki-Miyaura coupling, and one of a palladiumcatalyzed Buchwald-Hartwig cross-coupling, are available in the literature. State-of-the-art modeling performs extremely well on those high-quality datasets (R 2 > 0.8) , but the extremely focused chemical reaction space covered by HTE limits the predictions to a narrow scope of experimental conditions and reactants.