Automatic bug localization technologies can significantly alleviate the burden of debugging and maintaining software programs for developers. As a widely studied automatic bug localization technology, information retrieval-based bug localization has yielded promising performance of bug localization. In recent years, the utilization of deep learning for information retrieval-based bug localization has emerged as a research trend due to the widespread adoption of deep learning. This study systematically categorizes and summarizes 52 studies that have introduced deep learning to information retrieval-based bug localization in recent years. Firstly, a summary of datasets and evaluation indexes in this kind of bug localization is provided. Then, the localization performance of these techniques is analyzed from the perspectives of different granularity and transportability. Subsequently, information coding characterization methods and feature extraction methods employed in related studies are summarized. Finally, this study summarizes and compares the most advanced bug localization methods, and provides insights into the future directions of utilizing deep learning in information retrieval-based bug localization methods.