Given that MVS-A needs to be performed from scratch for each HTS dataset, it is crucial that the approach is computationally efficient. At most, our method required 5 seconds for the analysis of a single dataset on a 32 cores CPU, thus enabling quick processing of large collections of HTS data in a reasonable amount of time (Figure ). Furthermore, the time required for the analysis did not increase with the number of molecules screened in a given HTS dataset, meaning that this approach can be used on even large screening campaigns such as the ones typically performed in the industry. We further benchmarked the computational cost of MVS-A by repeating the entire analysis on a laptop with a 6 core CPU and 32GB of RAM. In this instance, MVS-A ran successfully on all datasets, taking less than 30 seconds per dataset and requiring at most 4GB of RAM (Figure ). This is in contrast with CatBoost object importance, which we could not run on the laptop due to out-of-memory errors.