We seek to identify musical correlates of real-world discovery behavior by analyzing users' audio identification queries from the Shazam service. Recent research has shown that such queries are not uniformly distributed over the course of a song, but rather form clusters that may implicate musically salient events. Using a publicly available dataset of Shazam queries, we extend this research and examine candidate musical features driving increases in query likelihood. Our findings suggest a relationship between musical novelty -- including but not limited to structural segmentation boundaries -- and ensuing peaks in discovery-based musical engagement.