TagDock is a computational toolkit we have developed to rapidly and efficiently generate plausible three-dimensional models for oligomeric biomolecular complexes. TagDock is designed to generate all geometrically feasible docking models, and does not utilize any scoring function to assess chemical or physical plausibility. Instead, we utilize experimental data such as distance measurements from techniques like solid-state NMR, EPR, or FRET spectroscopies to identify, or “filter” those docking solutions that are consistent with the experimental measurements. This strategy allows us to generate large numbers of candidate structures with minimal computational effort, and reduces the possibility that we might ignore or miss viable solutions because the computational expense becomes a limiting factor. Since the experimental data sets we use to filter solutions are generally sparse, containing perhaps only 5 – 10 distance measurements, we often generate multiple docking solutions that all satisfy the experimental constraints equally well. Our toolkit includes automatic assessment protocols, based on distance difference matrix analysis, that evaluates all accepted candidate structures and identifies additional distance measurements that would discriminate between candidate structures. In this way, the toolkit can be used to help plan future experiments by, e.g., pinpointing strategic positions for label incorporation for EPR or FRET studies. We will present several protein heterodimer complex examples to illustrate the performance and effectiveness of our method.