The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of re- search. While previous work has studied the supervised ver- sion of this problem, we study the more challenging case of unsupervised transductive transfer learning, where no la- beled data from the target domain are available at training. We describe some current state-of-the-art inductive and transductive approaches and then adapt these models to the problem of transfer learning for protein name extrac- tion. In the process, we introduce a novel maximum entropy based technique, Iterative Feature Transformation (IFT), and show that it achieves comparable performance with state-of-the-art transductive SVMs. We also show how sim- ple relaxations, such as providing additional information like the proportion of positive examples in the test data, can significantly improve the performance of some of the trans- ductive transfer learners.