This chapter focuses on three types of shape modalities, i.e. photos, hand-drawn sketches and 3D shapes. It reviews the specific challenges and the main datasets which have been used in cross-domain 3D shape retrieval. The chapter reviews the commonly proposed solutions that handle this new research problem. One of the common approaches used for cross-domain shape retrieval is based on Siamese network, a special type of neural network architectures. The main idea behind 3D shape-centric approaches is to place the 3D representation of the shape in the center, while the other representations are set around. A simple way to build an embedding shape space is to apply principal component analysis (PCA) to the set of the 3D shape signatures. In cross-domain shape retrieval, our aim is to place the different modalities depicting similar shapes closely in the embedding space. The intuitive solution is to train a model to project these modalities onto the embedding space.