We have introduced novel models to predict the chemical context for Buchwald-Hartwig coupling reactions. In particular, our models predict the chemical conditions jointly, i.e., given a query, it predicts a set of ligand, base, solvent, and (pre-)catalyst. We show that both single-label and multilabel models predict the chemical context well, reaching a top-3 accuracy above 90%. The model trained on multi-label data has better sensitivity on individual contexts and is better at predicting parts of the context. These findings show that there is a possibility of training a prediction model for a chemical context and that there is some advantage of including multi-label data. Although to fully explore the relative advantage of a multi-label model, another, more multi-labeled, dataset is needed. Overall, the current models can be used to predict the context of Buchwald-Hartwig reactions from a limited set of contexts, for example, in the scenario of an automated platform requiring a set of wellknown conditions. However, as shown by the time-dependency analysis, such models most likely need to be updated regularly to encompass changed commercial availability and novel science. This modeling requirement has been neglected in previous modeling studies and thus brings the relevancy of such models into question. We have clearly shown that, at least for Buchwald-Hartwig reactions, we cannot fully exploit historical data to predict more recent contexts, and it is likely that this is true for other reaction classes. On the other hand, from a chemical perspective, models based on historical data can be useful in some contexts and older contexts might be as chemically effective as newer ones. A potential avenue for future research is to augment the data driven-approach used herein with physics-based descriptors such as reactivity to develop models that can provide other types of recommendations. Nevertheless, we believe that the findings presented herein will be a template for creating models for other types of reactions. Although extra attention to the data and periodic re-trained is required, we envisage that such models will be useful in future synthesis planning.