Traditional database query optimization technique have as its main heuristic the organization of predicates in two main types: selection predicates and join predicates. Join predicates are considered much more expensive than selection predicates. In additional, it's also considered that there's no big difference among the costs of different selection predicates, what makes the optimizer executes them first in any order, reducing the number of tuples necessary to execute join predicates.This assumption, that is well applied in traditional database applications, becomes invalid in respect of recent database applications, that executes complex functions over complex data in selection predicates. In this cases, selection predicates are considered more expensive than join predicates and their costs cannot be considered equivalent anymore. This makes the main heuristic of push down selections invalid for these kind of new selection predicates which calls for new optimization techniques. These type of cue named expensive predicates. This work has two main objectives: Present a software that makes possible the development, test and integrat analisys of different algorithms for evaluating expensive predicates and analyse the performance of four algorithm's implementations that are based on Cherry Picking strategy, which aims at exploring the data dependency between input values to expensive predicates. The experiments considered conjunctive(AND) queries, and the general idea is to try evaluate the attributes in a order that minimizes the general cost of the tuples.