Reaction outcomes are susceptible to minute changes in reaction conditions, and consequently, getting the reaction conditions, or context, right is critical in organic synthesis. However, condition prediction has received relatively little attention compared to other CASP research areas, such as forward reaction prediction and retrosynthesis analysis. Although extensive efforts have sought to formalize the rules for condition optimization, initial conditions selection remains dependent on human intuition. Previously reported in silico methods typically only apply for specific reaction classes or parts of the reaction condition, e.g., solvent or catalyst. Gao et al., on the other hand, trained a neural network model to predict the chemical context (catalysts, solvents, and reagents) as well as the temperature most suitable for any given reaction, using 10 million reactions from Reaxys. The authors found a close match to the recorded context, i.e., the full set of reagents, within the top-10 suggestions in about 70% of the time, whereas the accuracy for individual species was much higher. Ryou et al. proposed a related model based on a graph neural network that predicts reaction conditions for four reaction classes. Their results are impressive for individual reaction species, but less competitive in the combination of the conditions, obtaining a top-3 accuracy of at most 70%. Maser et al. later refined the work of Ryou et al. to exploit the multi-label nature of the data, i.e. each reaction can have more than one recorded context. This work is, to our knowledge, the only multilabel approach reported for condition context prediction. The advantage of including multi-label data is that the model may assign probabilities to different chemical contexts rather than just predicting one. This property enables the model to be used in library generation. In such a scenario it will sometimes be necessary to select a context that is not the optimal for one particular reaction, but rather optimal for a set of reactions.