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It has never been easy to search relevant information from ever increasing corpus of academic literatures. Large volume of research exists concerning this problem. The previous solutions are put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems such as PubMed offer flexible query interface, but churn out a list of matching documents that users have to digest in order to find the answers to their queries. On the other hand, the "deep" search systems such as PPI Finder and iHOP return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to address this problem, we introduce a new search engine, BOSS, for search on biomedical objects. Unlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a minimal semantic unit such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in the segments, and aggregates the segments for each object. Finally, it turns up for the user the ranked list of the objects along with their matching segments. BOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because as it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS is believed to notch up the technological level of traditional solutions for search on biomedical information. BOSS is accessible at http://boss.korea.ac.kr.