Search engines are very effective at generating extensive lists of results that are highly relevant to user-provided query terms. But the lack of effective overviews challenges users who seek to understand these results, especially for complex tasks such as learning about a new topic, which require gaining overviews of and exploring large sets of search results, identifying unusual documents, and understanding their context. Categorizing the results into comprehensible visual displays using meaningful and stable classifications can support user exploration and understanding of large sets of search results. We conducted two exploratory studies to investigate categorized overviews of search results for complex search tasks within the domain of U. S. government web sites, using a hierarchy based on the federal government organization and a hierarchy generated by automated clustering. First we compared two overview conditions vs. a control (N=18) and found that participants performed better with and preferred the overview conditions. Then we compared automated clustering vs. an overview based on a government hierarchy (N=12) for several task types, and found that several factors appeared to influence the preferred overview. The studies motivated new requirements for the organization of and interaction with web search results: user-selectable organization of results and a lightweight mechanism for customizing hierarchies. The results were also used to refine a set of principles that we are developing for categorized search result visualization.