Computational exploration of chemical space is a powerful tool for designing organometallic homogeneous catalysts. While catalytic properties depend on ligand properties and spatial arrangement, the role of stereoisomerism in defining catalyst selectivity and reactivity has only been elucidated sporadically, leaving gaps in virtual screening workflows. This study investigates the necessity of exploring ligand configurations for virtual high-throughput (HT) screening of octahedral transition metal complexes. Using automated workflows, ligand configuration ensembles were generated for bisphosphine ligands with Ir(III), Ru(II), and Mn(I) metal centers. DFT calculations revealed distinct preferences for Ir(III) configurations, whereas Mn(I)- and Ru(II)-complexes displayed significant fluxionality, with multiple configurations within a 10 kJ mol−1 energy range. Linear regression analyses showed that global descriptors, such as bite angle and HOMO–LUMO gap, are transferable across configurations and metal centers, while local steric descriptors lacked such transferability. Machine learning (ML) models successfully classified ligand configurations (balanced accuracy >0.8) but struggled to predict stability across metal centers, especially for Mn(I) and Ru(II). Thus, improved descriptors of the first coordination sphere to capture fluxionality and stability more effectively can improve ML models. Overall, this study underscores the limitations of ignoring stereoisomerism in virtual HT screening, which may lead to incomplete exploration of chemical space and underrepresentation of key catalyst features. Until dynamic digital representations are developed, exhaustive stereoisomerism exploration should be implemented for screening workflows.