Previous studies have highlighted the high arrival rate of new contenton the web. We study the extent to which this new content can beefficiently discovered by a crawler. Our study has two parts. First,we study the inherent difficulty of the discovery problem using amaximum cover formulation, under an assumption of perfect estimates oflikely sources of links to new content. Second, we relax thisassumption and study a more realistic setting in which algorithms mustuse historical statistics to estimate which pages are most likely toyield links to new content. We recommend a simple algorithm thatperforms comparably to all approaches we consider.We measure the emphoverhead of discovering new content, defined asthe average number of fetches required to discover one new page. Weshow first that with perfect foreknowledge of where to explore forlinks to new content, it is possible to discover 90% of all newcontent with under 3% overhead, and 100% of new content with 9%overhead. But actual algorithms, which do not have access to perfectforeknowledge, face a more difficult task: one quarter of new contentis simply not amenable to efficient discovery. Of the remaining threequarters, 80% of new content during a given week may be discoveredwith 160% overhead if content is recrawled fully on a monthly basis.