Finding homologues for a given protein plays a major role in predicting the protein's structure and function. However, it is still difficult to find remote homologues with low sequence similarity, even with advanced sequence search methods. We propose a simple filtering method that uses predicted structural information, pertaining to secondary structures and solvent accessibilities. It filters the more promising homologues from the many candidate proteins obtained by PSI-BLAST with a less stringent threshold E-value. The final decision is made by a simple linear discrimination method, considering the E-value of PSI-BLAST and the statistical significance scores of structural matches. An in-house neural network program is used for the prediction of secondary structures and solvent accessibilities for both the query and library proteins. The performance of our filtering method was evaluated by the cross-validation method, using the SCOP superfamily relationship as the correct standard. Coverage-reliability plots show that our filtering method clearly improves the performance of PSI-BLAST. The secondary structure improves PSI-BLAST better than the solvent accessibilities, but the combination of these two features with PSI-BLAST leads to the best result. The advantage of our method is its easy implementation with fewer parameters to be tuned and faster computation. We also discuss its performance with predicted and observed secondary structures.