People engage in search episodes as they have a task or a problematic situation. Often this task is not clearly expressed by the information seeker, nor directly supported by the search system.People also list their tasks using tools such as to-do applications, and while many of these could be search tasks, there is a lack of that recognition or a possible bridge to a search system. In the work reported here, we aim to create that bridge by analyzing data on both sides. In task management, we examined 1,000 to-do tasks annotated by human assessors for their appropriateness for a search engine and created a simple process to learn that classification. In search, we analyzed millions of queries in a search engine log to understand how often queries represent tasks that people express in to-do lists. Our results show that (1) we can accurately predict which of the to-do tasks are appropriate as search queries; and (2) such tasks do indeed show up in search engines as a substantial segment. Together, these findings outline an opportunity to link explicitly expressed tasksto search queries and vice versa. This has implications for both task completion and query understanding.