We investigate the use of web search queries for detecting errors in non-native writing. Distinguishing a correct sequence of words from a sequence with a learner error is a baseline task that any error detection and correction system needs to address. Using a large corpus of error-annotated learner data, we investigate whether web search result counts can be used to distinguish correct from incorrect usage. In this investigation, we compare a variety of query formulation strategies and a number of web resources, including two major search engine APIs and a large web-based n-gram corpus.