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Fact verification has become an important task due to the increased popularity of blogs, discussion groups, and social sites, as well as of encyclopedic collections that aggregate content from many contributors. We investigate the task of automatically retrieving supporting evidence from the Web for factual statements. Using Wikipedia as a starting point, we derive a large corpus of statements paired with supporting Web documents, which we employ further as training and test data under the assumption that the contributed references to Wikipedia represent some of the most relevant Web documents for supporting the corresponding statements. Given a factual statement, the proposed system first transforms it into a set of semantic terms by using machine learning techniques. It then employs a quasi-random strategy for selecting subsets of the semantic terms according to topical likelihood. These semantic terms are used to construct queries for retrieving Web documents via a Web search API. Finally, the retrieved documents are aggregated and re-ranked by employing additional measures of their suitability to support the factual statement. To gauge the quality of the retrieved evidence, we conduct a user study through Amazon Mechanical Turk, which shows that our system is capable of retrieving supporting Web documents comparable to those chosen by Wikipedia contributors.