A "third wave" of Neural Network (NN) approaches, popularly referred to as "deep learning", has swept over speech recognition, computer vision, and natural language processing, leaving new standards of machine learning performance behind in its wake. This deep learning wave has recently begun to swell in Information Retrieval (IR) as well, and while Neural IR has yet to achieve the level of success deep learning has achieved in other areas, the recent surge of interest and work on Neural IR suggest that this state of affairs may be quickly changing. In this talk, I will survey the past and present landscape of Neural IR research, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings). I will highlight the successes of Neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research. For further reading, please see this very recent paper: https://arxiv.org/abs/1611.06792.