These findings have provided insight into the challenges associated with aggregated data for bioactivity modeling. Currently such agglomerated data is commonly used for modeling purposes and it has been reported that this comes at the cost of a large unexplained variance between measurements. However, until now, little has been done to address this problem. Here, we demonstrated that measured bioactivity values indeed vary strongly and that part of this variance is explained by the experimental context. Moreover, we showed that the presence of different assays in our datasets negatively influences model performance, although these effects are small and depend on the dataset.