An active learning library based on scikit and modAL python packages was adopted from another study. A set of molecules to initialise the active learning cycle can be selected via RDKit's MaxMin picker from the chemical space, or picked at random. The processing was parallelised using the python library Dask, which supports a diverse set of technologies, including the Slurm Workload Manager that is deployed ubiquitously on highperformance computing clusters. Dask is used to secure resources (scheduling workers on Slurm), submitting work and retrieving results. The three major computationally-expensive components were parallelised: 1) building and scoring of the molecules, 2) computing the Morgan fingerprints, and 3) computing the Tanimoto similarity across the chemical space for the Gaussian Process modelling.