We showed that including assay context can improve modeling results in some data sets, by explaining part of the variance present in the modeled data. Interestingly, for the kinase dataset using assay embeddings on top of the other inputs did not help. Assay descriptions have previously been shown to be useful for modeling by Seidl et al. who found that ChEMBL's assay descriptions can be used for zero-shot and few-shot learning. Here we aimed to improve models of targets with sufficient existing data. To this end we utilized multiple PCM models that include assay context. The embedding-based descriptor showed a consistent improvement for the largest dataset that was modeled, resulting in an R 2 increasing from 0.68 to 0.71 in the random split condition and from 0.60 to 0.65 in the scaffold split condition. For most of the other methods and datasets that were analyzed including assay context did not have an effect or even had a negative effect. Specifically, the bag-of-words fingerprint and the models with separate tasks for the assay topics decreased performance. These findings suggest that the heterogenous nature of assay descriptions requires assay embeddings in order to obtain a clear signal amongst this noisy information in order to improve performance. Additional embedding methods could be tested to see if the observed performance change can be increased. For example, to improve the performance for the bag-of-words approach one could attempt to select the used words / bits that improve modeling performance. The reason that assay embeddings perform well but that MT models with assay topics based on these embeddings have hardly any predictive performance could be due to the balance between data sparsity on one hand and cluster size on the other.