The data sets provided by Ahneman et al. and Perera et al. are of high quality but focus on reactants with very simple structures and limited functionalities. Thus, they can be considered idealised data sets that are not necessarily representative of real-world data collected in typical academic or industrial laboratories. These two data sets have been used extensively in literature in the context of predictive synthesis. (3,4) Recently, King-Smith and colleagues released a large HTE reaction set generated at Pfizer, containing examples of around 40 chemical transformation types. We processed, filtered and extracted entries for both (3) Buchwald-Hartwig and (4) Suzuki-Miyaura reactions, affording in total ca. one and two thousand records, respectively ( The resulting four HTE data sets were used with random and out-of-sample cross-validation splits (k=5). In the random splits, the training, validation and test sets are likely to contain the same reactants, reagents and products, and thus, they are more representative of chemical-development like approach, where the model does not need to generalise to unseen structures. To evaluate the model performance under different data set sizes, the training was performed with different fractions of the training data for each random split (100%, 50%, 25% and 10% of the available training data; see Table ). The out-of-sample splits were conducted with respect to the reactants, and thus, training, validation and test set each contained unique product structures (Figure ), allowing to assess the ability of the models to generalise to previously unseen compounds in the reaction schemes. The evaluation of model performance on such out-of-sample splits is arguably more closely related to a typical synthetic problem, where a number of novel compounds need to be synthesised using a specific reaction type. Each of the five out-of-sample validation splits for the set by