In this paper, we aimed to enhance the exploration of molecular representations for the surrogate fitness models, going beyond the recommendations and documentation of GAUCHE. Our investigation expanded to include the application of the Tanimoto kernel to a diverse set of fingerprints . In line with the extended connectivity fingerprints, we make use of feature-based connective fingerprints (FCFP4, FCFP6) which add internal labels, denoting whether an atom is basic or acidic, aromatic, halogen, or a hydrogen bond donor or acceptor. We also use the fingerprints included with RDKit (RDFP), atom pair fingerprints (APFP) which focus on local substructures, and topological torsion fingerprints (TTFP) which aim to predominantly capture long-range substructures. By incorporating this varied set of fingerprints into our study of GB-BI, we intend to cover a wider range of molecular characteristics and aim to evaluate the effectiveness of these different fingerprints in aligning with underlying structure-fitness relationships.