View-based 3-D model retrieval is one of the most important techniques in numerous applications of computer vision. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to evaluate the state-of-the-art methods. To tackle this problem, we systematically investigate and evaluate the related methods by: 1) proposing a clique graph-based method and 2) reimplementing six representative methods. Moreover, we concurrently evaluate both hand-crafted visual features and deep features on four popular datasets (NTU60, NTU216, PSB, and ETH) and one challenging real-world multiview model dataset (MV-RED) prepared by our group with various evaluation criteria to understand how these algorithms perform. By quantitatively analyzing the performances, we discover the graph matching-based method with deep features, especially the clique graph matching algorithm with convolutional neural networks features, can usually outperform the others. We further discuss the future research directions in this field.