Molecular docking plays a crucial role in structure-based drug discovery, enabling the prediction of how small molecules interact with protein targets. Traditional docking methods rely on scoring functions and search heuristics, whereas recent generative approaches, such as DiffDock, leverage deep learning for pose prediction. However, blind-diffusion-based docking often struggles with binding site localization and pose accuracy, particularly in complex protein–ligand systems. This work introduces GeoDirDock (GDD), a guided diffusion approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along geodesic paths within multiple spaces representing translational, rotational and torsional degrees of freedom. Our method leverages expert knowledge to direct the generative modelling process, specifically targeting desired protein–ligand interaction regions. We demonstrate that GDD outperforms existing blind docking methods in terms of root mean squared distance accuracy and physicochemical pose realism. Our results indicate that incorporating domain expertise into the diffusion process leads to more biologically relevant docking predictions. Additionally, we explore the potential of GDD as a template-based modelling tool for lead optimization in drug discovery through angle transfer in maximum common substructure docking, showcasing its capability to accurately predict ligand orientations for chemically similar compounds. Future applications in real-world drug discovery campaigns will naturally continue to refine and extend the utility of prior-informed diffusion docking methods.