The present study is aimed at incorporating biological assay context into bioactivity modeling. In order to make the assay descriptions that are available in ChEMBL machine interpretable, we compared a bag-of-words and an NLP-based embedding. In the first method, assay descriptions were converted to a bag-of-words based fingerprint (Figure 3). In the second method, the assay descriptions were embedded by the pretrained language model BioBERT. Subsequently, dimensionality reduction was applied to the resulting embeddings (Figure ). Coloring the datapoints based on properties from the ChEMBL metadata shows that this approach yields informative representations; for several relevant properties a separation is visible amongst the plotted records. The separation is most pronounced for the division between binding and functional assays and for the BAO format. The BAO format shows that within the functional assays there is a clear distinction between the records involving living organisms (orange, organism-based) and cell-based assays (green, cell-based). By coloring on assay tax id (i.e., the assay taxonomy) it also becomes clear that most binding assays and a large part of functional assays are done in human based systems (H. sapiens, red). The majority of experiments done in mice (M. musculus, blue) and rats (R. norvegicus, orange) cluster together and don't overlap with human-based systems. There is a marked overlap between confidence scores and assay types.