The sequential importance sampling method and its various modifications have been developed intensively and used effectively in diverse research areas ranging from polymer simulation to signal processing and statistical inference. We propose a new variant of the method, sequential importance sampling with pilot-exploration resampling (SISPER), and demonstrate its successful application in folding polypeptide chains described by a two-dimensional hydrophobic-hydrophilic (HP) lattice model. We show by numerical results that SISPER outperformed several existing approaches, e.g., a genetic algorithm, the pruned-enriched Rosenbluth method, and the evolutionary Monte Carlo, in finding the ground folding states of 2D square-lattice HP sequences. In a few difficult cases, the new method can find the ground states without using any prior structural information on the chain. We also discuss the potential applications of SISPER in more general problems.