An important aspect of creating a dataset for modeling is data curation. Bioactivity datasets, such as ChEMBL, make large sets of data from different sources available. For modeling, it is often assumed that data from different biological assays can be combined. However, this has been criticized and previous research has shown that reported half-maximal response concentrations (IC50 or EC50) values, inhibitory binding constants (Ki) and dissociation constants (KD) are noisy. Next to experimental error, this could in part be caused by assay heterogeneity; experiments vary in their format, target modifications, detection method, and endpoint, which can all affect the readout. Recently, multiple studies have been published on ways of combining data from different sources whilst preserving the assay context. One method to achieve this is by adding generic assay descriptors as an additional model input. The addition of a fingerprint denoting whether the assay is a binding or functional assay, i.e. measures a direct interaction or indirect effects, can lead to improvements in predictive performance. In addition to this information, ChEMBL contains free text assay descriptions that provide information on the aim, target and method of the experiment, and learned embeddings based on these descriptions have been shown to improve predictive quality, in context of zero-and few-shot learning. Another approach that has been explored is multitask modeling, where the model simultaneously makes predictions for various tasks, for example the assay categories, and derives both shared and task-specific parameters. This has been applied on broad assay categorizations such as the distinction between binding and functional experiments, different readouts, and on the detailed level of individual publications/records. Taken together, the recent interest in biological assay context has led to an improved predictive performance of models, and has hugely benefitted from the metadata available in ChEMBL. However, for the current metadata in ChEMBL, categorization is either very broad or unique to the specific publication that data originated from. Currently, assay-specific descriptions in ChEMBL are present in the form of free text and can therefore not easily be used to group similar assays.