There is a current trend towards clinical protocols which involve an initial "selection" phase followed by a hypothesis testing phase. The selection phase may involve a choice between competing treatments or different dose levels of a drug, between different target populations, between different endpoints, or between a superiority and a non-inferiority hypothesis. Clearly there can be benefits in elapsed time and economy in organizational effort if both phases can be designed up front as one experiment, with little downtime between phases. Adaptive designs have been proposed as a way to handle these selection/testing problems. They offer flexibility and allow final inferences to depend on data from both phases, while maintaining control of overall false positive rates. We review and critique the methods, give worked examples and discuss the efficiency of adaptive designs relative to more conventional procedures. Where gains are possible using the adaptive approach, a variety of logistical, operational, data handling and other practical difficulties remain to be overcome if adaptive, seamless designs are to be effectively implemented.