The features used in many current medical image retrieval systems are usually low-level hand-crafted features. This limitation may adversely affect the retrieval performance. To address this problem, this paper proposes a simple yet discriminative feature, called histogram of compressed scattering coefficients (HCSC), for medical image retrieval. In the proposed work, the scattering transform, a particular variation of deep convolutional networks, is first performed to yield more abstract representations of a medical image. A projection operation is then conducted to compress the obtained scattering coefficients for efficient processing. Finally, a bag-of-words (BoW) histogram is derived from the compressed scattering coefficients as the features of the medical image. The proposed HCSC takes the advantages of both scattering transform and BoW model. Experiments on three benchmark medical computer tomography image databases demonstrate that HCSC outperforms several state-of-the-art features.