notable among these are GuacaMol 2 , Tartarus , and the Therapeutics Data Commons . GuacaMol offers lightweight tasks focused on molecule rediscovery where the fitness of a generated molecule is assessed using the Tanimoto similarity 70,71 between the generated molecule and the target molecule, based on their respective extended-connectivity fingerprints. Tartarus and the Therapeutics Data Commons, conversely, present more challenging and computationally intensive benchmark tests utilising docking methods , which evaluate the theoretical affinity between a small molecule and a target protein. These benchmarking suites have been extensively employed, facilitating a fair and open comparison with other published methods for generating small molecules. GuacaMol has rediscovery tasks for three known drugs: Celecoxib (an anti-inflammatory), Troglitazone (an antidiabetic), and Thiothixene (an antipsychotic). Note that these three rediscovery tasks have been previously solved by several different methods 2 , while exhibiting varying levels of reliability and efficiency. In previous work , we used the hardest of these tasks, the rediscovery of Troglitazone, to quantify the efficiency between GB-EPI and GB-GA. In this paper, we apply the Troglitazone rediscovery task to assess the effectiveness of GB-BI. Unlike computationally demanding docking tasks, the evaluation of Tanimoto similarities incurs limited computational costs. This enables us to efficiently gather a substantial amount of statistical data on the performance of GB-BI with regard to the use of different molecular representations and acquisition functions. A drawback of the simple rediscovery tasks of GuacaMol is their relatively lack of discriminative capabilities .