Thanks to its computational efficiency, this pipeline takes only a few seconds on low-end hardware, even for large HTS datasets. Crucially, our approach relies exclusively on the HTS dataset of interest. As such, it does not rely on historical information of which compounds tend to be false positives for that assay technology (like e.g. PAINS), nor on assumptions of which biophysical process is causing the interference (e.g. aggregation or autofluorescence predictors). This means that our method is inherently applicable to any assay technology and any region of the chemical space, while being able to detect any type of interferent.