Guessing a low entropy value in multiple attempts
Bob's best bet is to guess the $t$ values with largest probability.

If you're willing to use Rényi entropy instead, Proposition 17 in Boztaş' Entropies, Guessing and Cryptography states that the error probability after $t$ guesses is at most
$$ 1 - 2^{-H_2(\mu)\left(1-\frac{\log t}{\log n}\right)} \approx \ln 2 \left(1-\frac{\log t}{\log n}\right) H_2(\mu), $$
where $n$ is the size of the domain. Granted, the dependency on $t$ is pretty bad, and perhaps Boztaş was focused on a different regime of the entropy.

For the Shannon entropy, you can try to solve the dual optimization problem: given a fixed failure probability $\delta$, find the maximal entropy of such a distribution. Using the convexity of $-x\log x$, we know that the distribution $\mu$ has the form $a,b,\ldots,b;b,\ldots,b,c$, where $a\geq b\geq c$, $a+(t-1)b = 1-\delta$, and $c = \delta-\lfloor\frac{\delta}{b}\rfloor b$. We have $t-1+\lfloor\frac{\delta}{b}\rfloor$ values that get probability $b$. Conditioning on $s = \lfloor\frac{\delta}{b}\rfloor$, we can try to find $b$ which minimizes the entropy. For the correct value of $s$, this will be an internal point (at which the derivative vanishes). I'm not sure how to get asymptotic estimates using this approach.