Searching through large structured data is a crucial task in enterprise as well as online search. Existing keyword-based search methods were mostly designed for web document search, and support a limited variety of query types suitable for structured data. Their main underlying drawback is the lack of semantic understanding of the data and users' natural language queries, resulting in a fundamental disconnect. In this paper, we bridge that gap through effective semantic type discovery and indexing of structured data. We demonstrate S3D (Semantic Search over Structured Data), a novel system that supports queries such as finding related tables, rows or columns, amongst others, on a large scale of structured data from multiple heterogeneous sources.