As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a website’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by bots. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. We believe these features formulate a basis for a production-level query stream classifier.