Precision-oriented search results such as those typically returned by the major search engines are vulnerable to issues of polysemy. When the same term refers to different things, the dominant sense is preferred in the rankings of search results. In this paper, we propose a novel two-box technique in the context of Web search that utilizes contextual terms provided by users for query disambiguation, making it possible to prefer other senses without altering the original query. A prototype system, Bobo, has been implemented. In Bobo, contextual terms are used to capture domain knowledge from users, help estimate relevance of search results, and route them towards a user-intended domain. A vast advantage of Bobo is that a wide range of domain knowledge can be effectively utilized, where helpful contextual terms do not even need to co-occur with query terms on any page. We have extensively evaluated the performance of Bobo on benchmark datasets that demonstrates the utility and effectiveness of our approach.