Three-dimensional (3D) retrieval of objects and models plays a crucial role in many application areas, such as industrial design, medical imaging, gaming and virtual and augmented reality. Such 3D retrieval involves storing and retrieving different representations of single objects, such as images, meshes or point clouds. Early approaches considered only one such representation modality, but recently the CMCL method has been proposed, which considers multimodal representations. Multimodal retrieval, meanwhile, has recently seen significant interest in the image retrieval domain. In this paper, we therefore explore the application of state-of-the-art multimodal image representations to 3D retrieval, in comparison to existing 3D approaches. In a detailed study over two benchmark 3D datasets, we show that the MuseHash approach from the image domain outperforms other approaches, improving recall over the CMCL approach by about 11% for unimodal retrieval and 9% for multimodal retrieval.