Compression algorithms for small strings - building upon / extending Huffman
A simple variant of Huffman is due, I believe, to David Wheeler.

Suppose the alphabet is $\Sigma = \{s_1, \dots, s_n\}$ and let $\star$ be some new character that's not in&nbsp;$\Sigma$. For each character $s\in\Sigma$, let $p(s)$ be the most common character to occur immediately after&nbsp;$s$ in your dataset.

To compress the string $X=x_1\dots x_N$, you first scan through it and, each time $x_{i+1}=p(x_i)$, replace $x_{i+1}$ with&nbsp;$\star$, giving a new string over alphabet $\Sigma\cup\{{\star}\}$. Then run ordinary Huffman on the resulting string. The idea is that the character&nbsp;$\star$ should be really common in the modified string, so it should get a really short codeword.

For example, if your alphabet is $\{a,b,c,d\}$ and $p(a)=a$, $p(b)=a$, $p(c)=d$ and $p(d)=c$, then the string $abacd$ would be transformed to $ab{\star}c{\star}$ and $cdcdcd$ would be transformed to $c{\star}{\star}{\star}{\star}{\star}$.

Since all your example strings begin with Z, it might also be worth storing the most common initial character to allow you to replace the first character of the string with&nbsp;$\star$, too.