Understanding how people interact with search engines is important in improving search quality. Web search engines typically analyze queries and clicked results, but these ac-tions provide limited signals regarding search interaction. Laboratory studies often use richer methods such as gaze tracking, but this is impractical at Web scale. In this paper, we examine mouse cursor behavior on search engine results pages (SERPs), including not only clicks but also cursor movements and hovers over different page regions. We: (i) report an eye-tracking study showing that cursor position is closely related to eye gaze, especially on SERPs; (ii) present a scalable approach to capture cursor movements, and an analysis of search result examination behavior evident in these large-scale cursor data; and (iii) describe two applica-tions (estimating search result relevance and distinguishing good from bad abandonment) that demonstrate the value of capturing cursor data. Our findings help us better under-stand how searchers use cursors on SERPs and can help design more effective search systems. Our scalable cursor tracking method may also be useful in non-search settings.