The purpose of this work was to evaluate a newly developed content-based retrieval approach for characterizing a range of different white blood cells from a database of imaged peripheral blood smears. Specimens were imaged using a 20x magnification to provide adequate resolution and sufficiently large field of view. The resulting database included a test ensemble of 96 images (1000x1000 pixels each). In this work, we propose a four-step content-based retrieval method and evaluate its performance. The content-based image retrieval (CBIR) method starts from white blood cell identification, followed by three sequential steps including coarse-searching, refined searching, and finally mean-shift clustering using a hierarchical annular histogram (HAH). The prototype system was shown to reliably retrieve those candidate images exhibiting the highest-ranked (most similar) characteristics to the query. The results presented here show that the algorithm was able to parse out subtle staining differences and spatial patterns and distributions for the entire range of white blood cells under study. Central to the design of the system is that it capitalizes on lessons learned by our team while observing human experts when they are asked to carry out these same tasks.