The topic of this communication is shape-similarity search for 3D-mesh models. We present and evaluate a composite 3D-shape feature vector (DESIRE), which is formed using depth buffer images, silhouettes, and ray-extents of a polygonal mesh. We contrast our method with the approach that is declared the best in the recent study. Our experiments suggest that the composite feature vector, which is extracted in a canonical coordinate frame, generally outperforms the competing method, which relies upon pairwise alignment of models. We also provide a Web-based retrieval system as well as publicly available executables for verifying the results.