Semantic search seems to be an elusive and fuzzy target to many researchers. One of the reasons is that the task lies in between several areas of specialization. In this extended abstract we review some of the ideas we have been investigating while approaching this problem. First, we present how we understand semantic search, the Web and the current challenges. Second, how to use shallow semantics to improve Web search. Third, how the usage of search engines can capture the implicit semantics encoded in the queries and actions of people. To conclude, we discuss how these ideas can create virtuous feedback circuit for machine learning and, ultimately, better search.