A social network represents the interconnected relations among people. In a knowledge-intensive era as of now, people have less capability to resolve more ill-defined and complicated problems. Several researches indicate that under such a circumstance people are more likely to turn to other people through their social networks than to consult sources like databases and documents. Searching in social networks is therefore an essential issue. In addition, typical social networks are neither regular nor completely random ones, but instead, they are mixtures between these two, which are referred to as small worlds. Consequently, such an issue is also called the small world search. Search mechanism in the small world can be classified into single-attribute approach (e.g. best connected) and multiple-attribute approach (e.g. social distance). Relevant research works, however, are mostly based on acquaintance networks. And one of the problems to search in acquaintance networks is its high attrition rate that hinders further search and results in low success rate. On the other hand, in recent years several researchers focus on the constitution and propagation of trust networks that represent the trustworthy relations among people. Since trust implies much closer to what we mean friends rather than acquaintance, we believe that the attrition rate in trust networks should be lower than in acquaintance networks. Based on this belief, we propose to search in trust networks rather than acquaintance networks to enhance the quality of the search process. We design three experiments to compare the search performance in the trust networks and in the acquaintance networks. Experiment I is to examine the ¡§social-distance¡¨ search strategy we adopt in the search. The second experiment evaluates the performance comparison without considering attrition. Finally, we consider the attrition rate and attrition rate difference for the comparison. The results show that as long as the attrition rate difference is beyond 10%, search in trust networks performs better than in acquaintance networks. It therefore justifies the feasibility of our proposed approach in gaining good search performance.