This paper considers how web search engines can learn from the successful searches recorded in their user logs. Document Transformation is a feasible approach that uses these logs to improve document representations. Existing test collections do not allow an adequate investigation of Document Transformation, but we show how a rigorous evaluation of this method can be carried out using the referer logs kept by web servers. We also describe a new strategy for Document Transformation that is suitable for long-term incremental learning. Our experiments show that Document Transformation improves retrieval performance over a medium sized collection of webpages. Commercial search engines may be able to achieve similar improvements by incorporating this approach.