This paper describes a method of introducing spatial consistency constraints in the process of matching set-based descriptors extracted from digital images. The proposed matching technique is guided by a rule that can be summarized as follows: a descriptor is important for the match if it is similar to some descriptor from the other image and its spatial neighbors are important. The resulting match is partial in the sense that it deliberately avoids the complexity of searching for one-to-one correspondences among particular descriptors, but established affinity among groups of descriptors instead.Formally, the proposed method is expressed as an eigenvalue problem, where the principal eigenvector's components render the importance values of individual descriptors, while the corresponding eigenvalue represents an estimate of the overall strength of affinity between images being matched. These measures of descriptor importance and image affinity are shown to provide a natural basis for intra- and inter-image prototype selection. Several variations of the proposed technique are empirically evaluated on the task of content-based image retrieval, demonstrating encouraging results.