Gene families are sets of homologous genes formed by duplications of a single original gene. Inferring their history in terms of gene duplications, gene losses and gene mutations yields fundamental insights into the molecular basis of evolution. However, phylogenetic inference of gene family evolution faces two difficulties: (i) the delimitation of gene families based on sequence similarity, and (ii) the fact that the models of evolution used for reconstruction are tested against simulated data that are produced by the model itself. Here, we show that digital genetics, or in silico experimental evolution, can provide thought-provoking synthetic gene family data, robust to rearrangements in gene sequences and, most importantly, not biased by where and how we think natural selection should act. Using aevol, a digital genetics modelwith an abstract phenotype but a realistic genome structure, we analyzed the evolution of 3,512 synthetic gene families under directional selection. The turnover of gene families in evolutionary runs was such that only 21% of those families would be accessible for classical phylogenetic inference. Extinct families showed patterns different from the final, observableones, both in terms of dynamics of gene gains and losses and in terms of gene sequence evolution. This study also reveals that gene sequence evolution, and thus evolutionary innovation, occurred not only through local mutations, but also through chromosomal rearrangements that re-assembled parts of existing genes.