In 2006, the New England Journal of Medicine selected medical imaging as one of the eleven most important innovations of the past 1,000 years, primarily due to its ability to allow physicians and researchers to visualize the very nature of disease. As a result of the broad-based adoption of micro imaging technologies, preclinical researchers today are generating terabytes of image data from both anatomic and functional imaging modes. In this paper we describe our early research to apply content-based image retrieval to index and manage large image libraries generated in the study of amyloid disease in mice. Amyloidosis is associated with diseases such as Alzheimer's, type 2 diabetes, chronic inflammation and myeloma. In particular, we will focus on results to date in the area of small animal organ segmentation and description for CT, SPECT, and PET modes and present a small set of preliminary retrieval results for a specific disease state in kidney CT crosssections.