Although there are numerous tools available, surprisingly the types of objective functions are not as many. Here we examined three popular objective functions. Theoretically, for each objective function we would test whether the score of the planted binding sites is superior to the scores of all other sets of words in the background sequences which are false positive predictions. This, of course, is impractical. In practice, we chose one tool that applies this objective function and compared the tool's prediction, which unfortunately is often a false positive, with the planted motif. If the planted motif has a better score, then the gap between the two scores shows the least extent to which the tool misses the global optimum of the objective function. On the other hand, if the prediction scores higher, it would suggest that the objective function is not accurate enough to model the true binding sites.