Search applications where queries are dependent on their context are becoming increasingly relevant in today's online applications. For example, the context may be the location of the user in locationaware search or the social network of the query initiator in socialaware search. Processing such queries efficiently is inherently difficult, and requires techniques that go beyond the existing, contextagnostic ones. A promising direction for efficient, online answering - especially in the case of top-k queries - is to materialize and exploit previous query results (views). We consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. We present algorithms that address these two problems, and illustrate their practical use in two important application scenarios: location-aware search and socialaware search. We validate our approaches via extensive experiments, using both synthetic and real-world datasets