We study the problem of contextual search in the adversarial noise model. Let d be the dimension of the problem, T be the time horizon and C be the total amount of noise in the system. For the ϵ-ball loss, we give a tight regret bound of O(C + d log(1/ϵ)) improving over the O(d^3 log(1/ϵ)) log^2(T) + C log(T) log(1/ϵ)) bound of Krishnamurthy et al (STOC'21). For the symmetric loss, we give an efficient algorithm with regret O(C+d log T). In terms of techniques, our algorithms are a departure from previous contextual search models in the sense that they keep track of density functions over the candidate vectors instead of a knowledge set consisting of the candidate vectors consistent with the feedback obtained.