We give efficient deterministic algorithms for converting randomized query algorithms into deterministic ones. We first give an algorithm that takes as input a randomized $q$-query algorithm $R$ with description length $N$ and a parameter $\varepsilon$, runs in time $\mathrm{poly}(N) \cdot 2^{O(q/\varepsilon)}$, and returns a deterministic $O(q/\varepsilon)$-query algorithm $D$ that $\varepsilon$-approximates the acceptance probabilities of $R$. These parameters are near-optimal: runtime $N + 2^{Ω(q/\varepsilon)}$ and query complexity $Ω(q/\varepsilon)$ are necessary. Next, we give algorithms for instance-optimal and online versions of the problem: $\circ$ Instance optimal: Construct a deterministic $q^\star_R$-query algorithm $D$, where $q^\star_R$ is minimum query complexity of any deterministic algorithm that $\varepsilon$-approximates $R$. $\circ$ Online: Deterministically approximate the acceptance probability of $R$ for a specific input $\underline{x}$ in time $\mathrm{poly}(N,q,1/\varepsilon)$, without constructing $D$ in its entirety. Applying the techniques we develop for these extensions, we constructivize classic results that relate the deterministic, randomized, and quantum query complexities of boolean functions (Nisan, STOC 1989; Beals et al., FOCS 1998). This has direct implications for the Turing machine model of computation: sublinear-time algorithms for total decision problems can be efficiently derandomized and dequantized with a subexponential-time preprocessing step.