Complex Answer Retrieval (CAR) is the process of retrieval for questions whose answers require many details or additional context to explain thoroughly [2]. These questions can be formulated as having a topic entity and a facet. For instance, for the question \Is cheese healthy?", the topic is \Cheese" and the facet is \Health e ects". We observe that some facets, such as \Health e ects", exhibit low utility: answers to questions about the health e ects of cheese are unlikely to use the terms directly. Instead, they will include related entities, such as nutrients or related diseases. We call these low-utility facets because the terms in the facet are not used directly in the text, and thus the terms themselves do not provide much value. In contrast, high-utility facets use language that is speci c to the topic and can be found directly in relevant answers (e.g., a facet of \Curdling' for the question \Why does cheese curdle?"); it would be di cult (and unlikely) that an answer to this this question does not include the term \curdle" or \curdling". In this talk, we propose a two-pronged approach for CAR by modifying a leading neural information retrieval architecture (PACRR [3]).