An atomic scale interpretation facilitates the assignment of functional properties to 3D reconstructions of macromolecular assemblies in electron microscopy (EM). Such a high-resolution interpretation is typically achieved by docking the known atomic structures of components into the volumetric EM maps. Docking locations are often determined by maximizing the cross-correlation coefficient of the two objects in a slow, exhaustive search. If time is of essence, such as in related visualization and image processing fields, the matching of data is accelerated by incorporating feature points that form a compact description of 3D objects. The complexity reduction afforded by the feature point representation enables a near-instantaneous matching. We show that such reduced matching can also deliver robust and accurate results in the presence of noise or artifacts. We therefore propose a novel multi-resolution registration technique employing feature-based shape descriptions of the volumetric and structural data. The pattern-matching algorithm carries out a hierarchical alignment of the point sets generated by vector quantization. The search-space complexity is reduced by an integrated tree-pruning technique, which permits the detection of subunits in large macromolecular assemblies in real-time. The efficiency and accuracy of the novel algorithm are validated on a standard test system of homo-oligomeric assemblies.