The normalized purity of labels annotated in ChEMBL showed that the resulting clusters enriched for similar properties, especially when small clusters were allowed. Interestingly, the records that clustered together generally had the same ChEMBL assay type (binding/functional) and BAO format but more diverse standard types. This was in line with the UMAP visualization, in which standard types did not occupy distinct regions. Further evaluation based on comparison to manually annotated labels showed that the assay clusters also grouped similar assays together on a detailed level. Here large minimum cluster sizes perform slightly better than small sizes. In general, the grouping of functional assays performed better than binding assays. One difference between these types of assay is that functional assays are much more diverse than binding assays. The functional assay descriptions, for that reason, often contain highly assay-specific information. Binding assays with different detection methods often only contain detailed information on the radioligand and target which is not relevant for the classification of the assay type. This is for example apparent for the binding assay clusters found for the adenosine receptors, as shown by the receptor and perturbagen specific topic-describing words (Table ). Overall, our qualitative analysis established that these words made sense and often referred to a specific type of experimental protocol. To facilitate the interpretation of these topics, we have made a Jupyter Notebook that shows how to use a pretrained large-language model to link the keywords to a type of pharmacological experiment (). It should be noted that the relevance of the assay descriptor is contingent on the specificity of the assay description. Specific assay conditions that can influence the outcome, e.g. the presence of an agonist, can only be taken into account when they are mentioned in the assay description.