Membrane proteins are crucial for many biological functions and have become attractive targets for pharmacological agents. About 10%-30% of all proteins contain membrane-spanning helices. Despite recent successes, high-resolution structures for membrane proteins remain exceptional. The gap between known sequences and known structures calls for finding solutions through bioinformatics. While many methods predict membrane helices, very few predict membrane strands. The good news is that most methods for helical membrane proteins are available and are more often right than wrong. The best current prediction methods appear to correctly predict all membrane helices for about 50%-70% of all proteins, and to falsely predict membrane helices for about 10% of all globular proteins. The bad news is that developers have seriously overestimated the accuracy of their methods. In particular, while simple hydrophobicity scales identify many membrane helices, they frequently and incorrectly predict membrane helices in globular proteins. Additionally, all methods tend to confuse signal peptides with membrane helices. Nonetheless, wet-lab biologists can reach into an impressive toolbox for membrane protein predictions. However, the computational biologists will have to improve their methods considerably before they reach the levels of accuracy they claim.