Previous work on reaction yield prediction 7,10 focused mainly on predictive yields on random splits of the data. However, the nature of scientific discovery pushes chemists to constantly explore new reaction chemical space. Thus, the reactions for which we want to make yield prediction are not sampled from a static distribution, but undergo continuous distribution shift . For instance, chemists are often interested in reactions including a novel substrate or a novel coupling partner (Fig. ). Therefore, validation on a random split is not necessarily informative of how a model would perform when used by chemists in a prospective fashion. This problem was underlined in recent publications , where machine learning algorithms showed far worse predictive performances when applied to out-of-sample data (e.g. on reactions with an additive not seen in the training set). While reported models performed very well on random splits (with an R 2 above 0.9, see Table ), these performances dropped significantly on some out-of-samples tests, with coefficient of correlations R 2 at best of 0.54 (obtained with a DFT model 6 ).