Interactions between proteins form the basis for many biological processes, and understanding their relationships is an area of active research. Computational approaches offer a way to facilitate this understanding without the burden of expensive and time-consuming experiments. Here, we introduce LatentDock, a generative model for protein-protein docking. Our method leverages a diffusion model operating within a geometrically-structured latent space, derived from an encoder producing roto-translational invariant representations of protein complexes. Critically, it is able to perform flexible docking, capturing both backbone and side-chain conformational changes. Furthermore, our model can condition on binding sites, leading to significant performance gains. Empirical evaluations show the efficacy of our approach over relevant baselines, even outperforming models that do not account for flexibility.