We present the results of the first large-scale Turkish information retrieval experiments performed on a TREC-like test collection. The test bed, which has been created for this study, contains 95.5 million words, 408,305 documents, 72 ad hoc queries and has a size of about 800MB. All documents come from the Turkish newspaper Milliyet. We implement and apply simple to sophisticated stemmers and various query-document matching fonctions and show that truncating words at a prefix length of 5 creates an effective retrieval environment in Turkish. However, a lemmatizer-based stemmer provides significantly better effectiveness over a variety of matching functions.