Uniform bound for rare events

Consider a family $F$ of subsets of a probability space $\Omega$. Assume that $F$ has bounded VC dimension and that the measure of each subset in $F$ is at least $\epsilon$.

Drawing $n$ iid points from $\Omega$, can we find a good lower bound on the probability that all subsets in $F$ contain at least a point?

I tried using the uniform CLT (VC inequality), but it seems suboptimal in the regime where the expected number of points in each subset is small.

The worst-case scenario is when elements of $F$ are disjoint. Let $k$ be the number of elements of $F$. Then, using this answer with Stirling numbers, the probability that all those elements are occupied is
$$\sum_i {\binom ni} (k\epsilon)^i (1-k\epsilon)^{n-i} \left\{ \begin{array}& i \\ k \end{array} \right\} k!/k^i$$
$$=k!\sum_i {\binom ni} \left\{ \begin{array}& i \\ k \end{array} \right\} \epsilon^i (1-k\epsilon)^{n-i}$$
If $\epsilon$ is small in the sense that $n\epsilon<k$, then it may be enough to use the lower bound from the $i=k$ term: $$\epsilon^k (1-k\epsilon)^{n-k}n! /(n-k)!$$